This paper focuses on the distributed online convex optimization problem with time-varying inequality constraints over a network of agents, where each agent collaborates with its neighboring agents to minimize the cumulative network-wide loss over time. To reduce communication overhead between the agents, we propose a distributed event-triggered online primal-dual algorithm over a time-varying directed graph. With several classes of appropriately chose decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. Finally, a numerical simulation example is provided to verify the theoretical results.

翻译：本文研究一类智能体网络中的带有时变不等式约束的分布式在线凸优化问题，其中每个智能体通过与相邻智能体协作，以最小化随时间累积的网络整体损失。为降低智能体间的通信开销，我们提出了一种基于时变有向图的分布式事件触发在线原始-对偶算法。通过选取恰当的多类递减参数序列与非递增事件触发阈值序列，我们建立了动态网络遗憾与网络累积约束违反度的界。最后，通过数值仿真算例验证了理论结果。