Xi-Ren Cao is a chair professor of Shanghai Jiao Tong University and an affiliate member of the Institute for Advanced Study at the Hong Kong University of Science and Technology (HKUST). He has worked as a consulting engineer for Digital Equipment Corporation, a research fellow at Harvard University, and a reader, professor, and chair professor at HKUST. He owns three patents in data- and tele- communications and has published three books in the areas of performance optimization and discrete event dynamic systems. Selected honors include being Fellow of IEEE and IFAC and best paper awards from the IEEE Control Systems Society and the Institution of Management Science. He has served as the Editor-in-Chief of Discrete Event Dynamic Systems: Theory and Applications, as an Associate Editor at Large of the IEEE Transactions of Automatic Control, as a Member of the Board of Governors of the IEEE Control Systems Society, and as a Member on the Technical Board of IFAC. His current research areas include financial engineering, stochastic learning and optimization, performance analysis of economic systems, and discrete event dynamic systems. He holds a PhD degree from Harvard University.
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.
