Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    Stochastic mirror descent method for distributed multi-agent optimization

    Access Status
    Fulltext not available
    Authors
    Li, J.
    Li, G.
    Wu, Z.
    Wu, Changzhi
    Date
    2016
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Li, J. and Li, G. and Wu, Z. and Wu, C. 2016. Stochastic mirror descent method for distributed multi-agent optimization. Optimization Letters: pp. 1-19.
    Source Title
    Optimization Letters
    DOI
    10.1007/s11590-016-1071-z
    ISSN
    1862-4472
    School
    Department of Construction Management
    URI
    http://hdl.handle.net/20.500.11937/4712
    Collection
    • Curtin Research Publications
    Abstract

    © 2016 Springer-Verlag Berlin HeidelbergThis paper considers a distributed optimization problem encountered in a time-varying multi-agent network, where each agent has local access to its convex objective function, and cooperatively minimizes a sum of convex objective functions of the agents over the network. Based on the mirror descent method, we develop a distributed algorithm by utilizing the subgradient information with stochastic errors. We firstly analyze the effects of stochastic errors on the convergence of the algorithm and then provide an explicit bound on the convergence rate as a function of the error bound and number of iterations. Our results show that the algorithm asymptotically converges to the optimal value of the problem within an error level, when there are stochastic errors in the subgradient evaluations. The proposed algorithm can be viewed as a generalization of the distributed subgradient projection methods since it utilizes more general Bregman divergence instead of the Euclidean squared distance. Finally, some simulation results on a regularized hinge regression problem are presented to illustrate the effectiveness of the algorithm.

    Related items

    Showing items related by title, author, creator and subject.

    • Applications of Linear and Nonlinear Models
      Grafarend, E.; Awange, Joseph (2012)
      Here we present a nearly complete treatment of the Grand Universe of linear and weakly nonlinear regression models within the first 8 chapters. Our point of view is both an algebraic view as well as a stochastic one. For ...
    • Leader Tracking of Euler-Lagrange Agents on Directed Switching Networks Using a Model-Independent Algorithm
      Ye, Mengbin ; Anderson, B.D.O.; Yu, C. (2019)
      In this paper, we propose a discontinuous distributed model-independent algorithm for a directed network of Euler-Lagrange agents to track the trajectory of a leader with nonconstant velocity. We initially study a fixed ...
    • Event-triggered algorithms for leader-follower consensus of networked euler-lagrange agents
      Liu, Q.; Ye, Mengbin ; Qin, J.; Yu, C. (2019)
      This paper proposes three different distributed event-triggered control algorithms to achieve leader-follower consensus for a network of Euler-Lagrange agents. We first propose two model-independent algorithms for a ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.