EMIS 8384: Stochastic Programming (G)
Stochastic programming (SP) is a systematic framework for modeling optimization problems that involve uncertain data and find optimal decisions for the same. SP draws upon tools and methods developed in many disciplines including operations research, mathematics and statistics. In recent years SP has gained significant attention as the work-horse for problems arising in a wide variety of applications including financial planning, industrial engineering, power systems planning and operations, computer and telecommunication networks etc. This course will introduce the main themes and methods in SP, and is suitable for students with basic knowledge of linear programming, stochastic processes and elementary analysis. In-class illustrations and a course project will involve implementations in C/C++. Homework will consist assignments on modeling, algorithmic enhancements and construction of proofs.
Schedule: Tuesdays and Thursdays 2:00 – 3:20 pm at Caruth Hall 0383.
- Alexander Shapiro, Darinka Dentcheva and Andrzej Ruszczyński, Lectures on Stochastic Programming: Modeling and Theory, MOS-SIAM Series on Optimization, second edition, 2014, ISBN: 978-1-611973-42-6.
- Stein W. Wallace and William T. Ziemba, Applications of Stochastic Programming, MOS-SIAM Series on Optimization, 2005, ISBN: 978-0-89871-555-2.
- Introduction to Stochastic Programming, Springer New York, tenth edition, 2011, ISBN: 978-1-4614-0236-7 (Online: 978-1-4614-0237-4). ,
Syllabus: PDF (Spring 2020)