Distributed event-triggered algorithm for unconstrained convex optimisation over weight-balanced directed networks

In this study, the authors propose a distributed discrete-time algorithm for unconstrained optimisation with event-triggered communication over weight-balanced directed networks. They consider a multi-agent system where each agent has a state and an auxiliary variable for the estimates of the optimal solution and the average gradient of the entire cost function. Agents send the states and auxiliary variables to their neighbours when their trigger errors exceed thresholds. They derive a convergence rate of the proposed algorithm which shows faster convergence to the optimal solution compared to the subgradient-based method.