Distributed online constrained convex optimization with edge-based event-triggered communication mechanisms
The implementation of existing distributed online optimization methods typically relies on real-time infor-mation exchange between nodes in a communication network,which incurs unsustainable resource consumption such as communication bandwidth in practical applications.To reduce communication costs,edge-based event-triggering technology is applied to distributed online constrained convex optimization,where each agent only knows its own time-varying local objective functions,and the common goal of all agents is to calculate the optimal sequence of solu-tions to minimize the total network objective value(the sum of all local objective functions).Firstly,an edge-based event-triggering mechanism is designed for the distributed online gradient descent algorithm under the assumption of a fixed strongly connected directed graph.Then,based on the designed edge-based event-triggering mechanism,an up-per bound for each agent's Regret is established,which is found to be directly related to the event-triggering thresh-old.Further analysis reveals that as long as the edge-based event-triggering threshold converges to zero over time,the Regret exhibits sublinear growth.Finally,the effectiveness of the proposed algorithm is verified through numerical simulation experiments oriented towards time-varying economic dispatch and diabetes classification prediction prob-lems.
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