STOCHASTIC PROGRAMMING
Stochastic programming is concerned with decision making in the presence of uncertainty, where the eventual outcome depends on a future random event. Topics include modeling uncertainty in optimization problems, risk measures, stochastic programming algorithms, approximation and sampling methods, and applications. Students are strongly encouraged to have knowledge of linear programming (e.g., MATH/COMP SCI/I SY E/STAT 525 ) and probability and statistics (e.g., MATH/STAT 431 ). Knowledge of integer optimization ( MATH/COMP SCI/I SY E 728 ) is helpful, but not required.
Topics
- modeling uncertainty in optimization problems
- risk measures
- stochastic programming algorithms
- approximation and sampling methods
- and applications
Topics explicitly listed in official catalogue description
Prerequisites & course information
Requisites: Graduate/professional standing Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Spring 2025 Learning Outcomes: 1. Learn the terms, basic capabilities, and limitations of stochastic programming models Audience: Graduate 2. Formulate stochastic programming models Audience: Graduate 3. Implement the algorithms used to solve stochastic programming problems Audience: Graduate 4. Learn principles of decomposition algorithms for solving large-scale optimization problems Audience: Graduate Learning Outcomes: 1. Learn the terms, basic capabilities, and limitations of stochastic programming models Audience: Graduate 2. Formulate stochastic programming models Audience: Graduate 3. Implement the algorithms used to solve stochastic programming problems Audience: Graduate 4. Learn principles of decomposition algorithms for solving large-scale optimization problems Audience: Graduate
Sources
- Course catalogue ↗2026-2027
- Catalogue entry ↗
Catalogue variants
- STOCHASTIC PROGRAMMING · 3 credits.