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Decentralized multi-agent optimization usually relies on information exchange between neighboring agents, which can incur unaffordable communication overhead in practice. To reduce the communication cost, we apply event-triggering technique to the decentralized multi-agent online convex optimization problem, where each agent is associated with a time-varying local loss function and the goal is to minimize the accumulated total loss (the sum of all local loss functions) by choosing appropriate actions sequentially. We first develop an event-triggered decentralized online subgradient descent algorithm for the full information case, where the local loss function is fully revealed to each agent at each time. We establish an upper bound for the regret of each agent in terms of the event-triggering thresholds. It is shown that the regret is sublinear provided that the event-triggering thresholds converge to zero as time …