[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.
