Advanced Optimization Techniques

Undergraduate

This course covers fundamental theory and algorithms in optimization, emphasizing convex optimization. Topics covered include convex analysis; duality and KKT conditions; subgradient methods; interior point methods; semidefinite programming; distributed methods; stochastic gradient methods; robust optimization; and an introduction to nonconvex optimization. Applications accepted from statistics & data science, economics, engineering, and the sciences. Prerequisites: Knowledge of linear algebra, such as MATH 222, 225; multivariate calculus, such as MATH 120; probability, such as S&DS 241/541; optimization, such as S&DS 431/631; and, comfort with proof-based exposition and problem sets. TTh 1:05pm-2:20pm