Optimization is a branch of applied mathematics focused on algorithms to determine maxima and minima of functions, often under constraints. Applications range from economics and finance to machine learning and information retrieval. This course will first develop advanced linear algebra tools, and then will study methods of convex optimization, including linear, quadratic, second-order cone, and semidefinite models. Several applications will be explored, and algorithms will be implemented using mathematical software to aid numerical experimentation.
Requisite: MATH 211 and either 271 or 272, or permission of the instructor. Limited to 30 students. Spring semester. Professor Leise.
If Overenrolled: Preference will be given to pre-registered students, math majors, and juniors and seniors.