ORIE 6326: Convex OptimizationLogistics
OverviewConvex optimization generalizes leastsquares, linear and quadratic programming, and semidefinite programming, and forms the basis of many methods for nonconvex optimization. This course focuses on recognizing and solving convex optimization problems that arise in applications, and introduces a few algorithms for convex optimization. Topics include: Convex sets, functions, and optimization problems. Basics of convex analysis. Leastsquares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems. Optimality conditions, duality theory, theorems of alternative, and applications. Algorithms: interiorpoint, subgradient, proximal gradient, splitting methods such as ADMM. Applications to statistics and machine learning, signal processing, control and mechanical engineering, and finance. Prerequisites: Strong working knowledge of linear algebra, a modern scripting language (such as Python, Matlab, Julia, R). Announcements
