Distributed Online Optimization With Dynamic Coupling Constraints Under Time-Varying Communication Delays

This paper considers distributed online optimization with dynamic inequality constraints under time-varying communication delays. A group of agents cooperatively estimates an optimal strategy by exchanging the information on the loss value that is sequentially disclosed to each agent. We develop a distributed primal-dual algorithm for an enlarged multiagent network with delayed agents that handle the delayed information. To analyze the optimality and feasibility of the algorithm, a dynamic regret function and an accumulated fit function are considered. We show that both the dynamic regret and fit functions achieve sublinear bounds even in the presence of communication delays. The numerical example verifies the theoretical claims of the sublinearity of the proposed method.

Automated summary

This paper investigates distributed online optimization with dynamic inequality constraints under time-varying communication delays. A group of agents cooperatively estimate an optimal strategy by exchanging sequentially disclosed loss value information. The authors propose a distributed primal-dual algorithm for an enlarged multiagent network with delayed agents, which handles the delayed information. Theoretical analysis shows that the algorithm achieves sublinear bounds for both the dynamic regret and fit functions, even in the presence of communication delays. Numerical examples validate the sublinearity of the proposed method.