Distributed Optimization With Event-Triggered Communication via Input Feedforward Passivity

In this letter, we address the distributed optimization problem with event-triggered communication by notion of input feedforward passivity (IFP). First, analyze continuous-time algorithm over uniformly jointly strongly connected balanced digraphs in an IFP-based framework. Then, propose a mechanism for algorithm. Next, discretize forward Euler method constant stepsize irrelevant to network size, and show that discretization can be seen as stepsize-dependent degradation passivity. Thus, discretized system preserves IFP property enables same but without Zeno behavior due discrete-time nature. Finally, numerical example is presented illustrate our results.