ORF 544 – Stochastic Optimization and Learning
Professor Warren Powell
Department of Operations Research and Financial Engineering
If you are attending the course (even just listening) make sure your name (and email) is on the list below).
Making decisions under uncertainty is a universal human activity, something we have all done our entire lives, and generally something we do every day. The study of this rich problem area can be organized under a broad umbrella called “stochastic optimization.” Normally taught as a mathematically deep topic, ORF 544 will be taught with a primary emphasis on proper modeling, and the design and analysis of practical algorithms.
The course will present a unified framework for stochastic optimization that cuts across the many communities that contribute to the general problem of the design and control of systems under uncertainty (I call this the “jungle of stochastic optimization”). This modeling framework has been tested in problem domains spanning transportation and logistics, energy, health, finance, internet search, and even the laboratory sciences.
Audience: The course is appropriate for students in operations research, economics, computer science, applied math, and any field of engineering (e.g. for students interested in engineering controls). It is open to undergraduates with a strong interest in models and algorithms. The course requires a basic background in probability and statistics at the undergraduate level (e.g. ORF 245 – if you have ORF 309, even better). There is a small amount of material where a background in linear programming is useful, but this will not be required on problem sets or exams. I will occasionally bridge to more advanced probabilistic concepts, but this will be aimed at students without this background and is not required.
Readings: The course will be taught from a new book, Stochastic Optimization and Learning: A Unified Framework, that is being written. To download the book:
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The book will introduce students to different notational systems, and will cover problems, modeling frameworks and algorithmic strategies from all of the books shown above.
Lecture slides for spring, 2019.
I will do my best to make my slides available before each lecture: