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Course: STAT 31015=TTIC 31070, BUSN 36903, CAAM 31015, CMSC 35470
Title: Convex Optimization
Instructor(s): Nathan Srebro
Teaching Assistant(s): Haoyang Liu and Gregory Naisat
Class Schedule: Sec 01: MW 12:05–1:20 PM in Social Sciences 122
Office Hours:  
Textbook(s): Boyd, Convex Optimization
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.

Prerequisite(s): STAT 30900/CMSC 37810