Course: CAAM 31015=STAT 31015, TTIC 31070, BUSN 36903, CMSC 35470
Title: Mathematical Computation IIA: Convex Optimization
Instructor(s): Mihai Anitescu
Teaching Assistant(s): TBA
Class Schedule: Sec 01: MW 4:10 PM–5:30 PM (Remote)
Textbook(s): Boyd, Convex Optimization
Prerequisite(s): STAT 30900/CMSC 37810
Description: This course covers the fundamentals of convex optimization.
Topics will include basic convex geometry and convex analysis, KKT condition, Fenchel and Lagrange duality theory; six standard convex optimization problems and their properties and applications: linear programming, geometric programming, second-order cone programming, semidefinite programming, linearly and quadratically constrained quadratic programming. In the last part of the course we will examine the generalized moment problem --- a powerful technique that allows one to encode a wide variety of problems (in probability, statistics, control theory, financial mathematics, signal processing, etc) and solve them or their relaxations as convex optimization problems.