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In many practical systems, such as engineering, social, and financial systems, control decisions are made only when certain events happen. This is either because of the discrete nature of sensor detection and digital computing equipment, or the limitation of computing power, which makes state-based control infeasible due to the huge state spaces involved. The performance optimization of such systems is generally different from traditional optimization approaches, such as Markov decision processes, or dynamic programming. In this talk, we introduce, in an intuitive manner, a new optimization framework called event-based optimization. This framework has a wide applicability to the aforementioned systems. With performance potential as building blocks, we develop optimization algorithms for event-based optimization problems. The optimization algorithms are first proposed based on intuition, and theoretical justifications are then given with a performance sensitivity based approach. Finally, we provide a few practical examples to demonstrate the effectiveness of the event-based optimization framework. We hope this framework may provide a new perspective to the optimization of the performance of event-triggered dynamic systems.
