STAT 31000/CMSC 37811. Mathematical Computation II — Convex Optimization

Department of Statistics
University of Chicago
Winter 2011

This course covers the fundamentals of continuous optimization, linear programming, and convex optimization. Students are expected to have a solid grounding in multivariate calculus and linear algebra, and will be expected to complete several substantial programming projects (using MATLAB) during the course.

Topics covered will include the simplex algorithm, duality, line search and trust region methods for unconstrained optimization, nonlinear equations and nonlinear least-squares problems, interior-point and augmented Lagrangian methods for constrained optimization, and semi-definite programming.

This web page is for the second part of the course. The web page for the first part is here. The main focus of this part will be the following three convex optimization techniques: geometric programming, second-order cone programming, semidefinite programming.



Location: Harper Memorial, Room 102

Times: 1:30–2:50PM on Tue/Thu.

Course staff

Instructors: Mihai Anitescu (Part I) and Lek-Heng Lim (Part II)
Office: Eckhart 122
Tel: (773) 702-4263
Office hours: Wed 1:30-2:30 PM, Thu 3:00–4:00 PM

Course Assistant: Han Han
Office: Ryerson N375
Office hours: TBA

Problem Sets (Part II)

Problem set will be assigned weekly and will be due the following week. Collaborations are permitted but you will need to write up your own solutions.

Bug report on the problem sets or the solutions: lekheng(at)


Grade composition: No in-class examination. Grade based entirely on eight take-home problem sets.