Focuses on the theory and algorithms that arise in nonlinear finite-dimensional optimization. Topics include line-search and trust region methods, quasi-Newton methods, and conjugate gradient methods.
Prerequisite:MA 301, MA 302. Sessions Typically Offered:Fall/Spring/Summer Years Typically Offered:Annually