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This paper focuses on the distributed online convex optimization problem withtime-varying inequality constraints over a network of agents, where each agentcollaborates with its neighboring agents to minimize the cumulativenetwork-wide loss over time. To reduce communication overhead between theagents, we propose a distributed event-triggered online primal-dual algorithmover a time-varying directed graph. Dynamic network regret and networkcumulative constraint violation are leveraged to measure the performance of thealgorithm. Based on the natural decreasing parameter sequences, we establishsublinear dynamic network regret and network cumulative constraint violationbounds. The theoretical results broaden the applicability of event-triggeredonline convex optimization to the regime with inequality constraints. Finally,a numerical simulation example is provided to verify the theoretical results.

Dynamic Regret and Cumulative Constraint Violation Analysis for  Distributed Online Constrained Convex Optimization with Event-Triggered  Communication