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[01247] Gradient-push algorithm for distributed optimization with event-triggered communications

  • Session Time & Room : 2C (Aug.22, 13:20-15:00) @F312
  • Type : Contributed Talk
  • Abstract : Decentralized optimization problems consist of multiple agents connected by a network. The agents have each local cost function, and the goal is to minimize the sum of the functions cooperatively. In this work, we propose a gradient-push algorithm involving event-triggered communication on a directed network. The convergence of the algorithm is established under suitable decays and summability conditions on a stepsize and triggering threshold.
  • Classification : 47Nxx, 65Kxx, Decentralized Optimization
  • Format : Talk at Waseda University
  • Author(s) :
    • jimyeong kim (Sungkyunkwan University)
    • Woocheol Choi (Sungkyunkwan Univeristiy)