Synopsis
This book is about making decisions driven by experience. In this context, a scenario is an observation that comes from the environment, and scenario optimization refers to optimizing decisions over a set of available scenarios. Scenario optimization can be applied across a variety of fields, including machine learning, quantitative finance, control, and identification. The scenario approach has been given a solid mathematical foundation in recent years, addressing fundamental questions such as: How should experience be incorporated in the decision process to optimize the result? How well will the decision perform in a new case that has not been seen before in the scenario sample? And how robust will results be when using this approach? This concise, practical book provides readers with an easy access point to make the scenario approach understandable to nonexperts, and offers an overview of various decision frameworks in which the method can be used. It contains numerous examples and diverse applications from a broad range of domains, including systems theory, control, biomedical engineering, economics, and finance. Practitioners can find "easy-to-use recipes," while theoreticians will benefit from a rigorous treatment of the theoretical foundations of the method, making it an excellent starting point for scientists interested in doing research in this field. Introduction to the Scenario Approach will appeal to scientists working in optimization, practitioners working in myriad fields involving decision-making, and anyone interested in data-driven decision-making.
About the Author
Marco C. Campi is a professor at the University of Brescia, Italy, where he has taught topics related to data-driven and inductive methods for many years. He is a distinguished lecturer of the Control Systems Society and chair of the Technical Committee IFAC on Modeling, Identification, and Signal Processing. Professor Campi has held visiting and teaching appointments at several institutions and has served in various capacities on the editorial boards of Automatica, Systems and Control Letters, and the European Journal of Control. Professor Campi is a Fellow of IEEE, a member of IFAC, and a recipient of the Giorgio Quazza prize and the IEEE CSS George S. Axelby outstanding paper award. He has delivered plenary addresses at major conferences, including Optimization, CDC, MTNS, and SYSID. Simone Garatti is an associate professor in automatic control at the Polytechnic University of Milan, Italy, where he received his Ph.D. in information technology engineering. Professor Garatti has been a visiting scholar and invited lecturer at various prestigious universities, where he has shared his research in optimization and scenario theory. In 2006, he won a fellowship for the short-term mobility of researchers provided by the National Research Council of Italy (CNR). His scientific interests include data-driven optimization, data-driven control systems design, system identification, and randomized algorithms for problems in systems and control. He is the author of more than 70 contributions in international journals, books, and proceedings.
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