STAT 31015. Mathematical Computation IIA — Convex Optimization

Department of Statistics
University of Chicago
Winter 2015

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.



Location: Eckhart Hall, Room 133

Times: 2:30–3:20pm on Mon/Wed/Fri.

Course staff

Instructor: Lek-Heng Lim
Office: Eckhart 122
Tel: (773) 702-4263
Office hours: Fri 3:30–5:00 PM

Course Assistant: Marc Goessling
Office: Eckhart 131
Office hours: Tue 4:00–5:00 PM

Problem Sets

Bug report on the problem sets: lekheng(at)


Grade composition: Two in-class quizzes (Feb 2 and Mar 11) and programming assignment.