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 3:00 PM–4:20 PM in Saieh 203
Textbook(s)
Boyd, 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.