Optimization for Decision Science

(3 Credit Hours) · Undergraduate

This course provides a practical introduction to models, algorithms, and modern software for large-scale numerical optimization, especially for decision-making in engineering and business contexts. Topics include (nonconvex) nonlinear programming, deterministic global optimization, integer programming, dynamic optimization, and stochastic programming. Multi-objective optimization, optimization with embedded machine learning models as constraints, optimal experiment design, optimization for statistical inference, and mathematical programs with complementarity constraints may be covered based on time and student interests. The class is designed for advanced undergraduate/graduate engineering, science, mathematics, business, and statistics students who wish to incorporate computation optimization methods into their research. The course begins with an introduction to modeling and the Python-based Pyomo computational environment. Optimization theory and algorithms are emphasized throughout the semester.