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：本文研究多智能体网络中带时变不等式约束的分布式在线凸优化问题，其中每个智能体与相邻智能体协作以最小化整个网络随时间累积的损失。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。通过选取多类适当递减的参数序列和非递增的事件触发阈值序列，我们建立了动态网络遗憾度与网络累积约束违反量的边界。最后通过数值仿真算例验证了理论结果。