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

Department of Statistics
University of Chicago
Winter 2012

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.

The first part of this course will focus on techniques that form the basis for large scale optimization problems, namely, iterative methods for solving large sparse linear systems. We will emphasize connections to the second part of the course on nonlinear programming and to other courses in UChicago Optimization sequence:

We will discuss the following stationary methods:

the following semi-iterative methods: and the following Krylov subspace methods: We will cover some basic ideas for preconditioning and stopping conditions.

This web page is for the first part of the course. The web page for the second part is here.



Location: Eckhart Hall, Room 117

Times: 3:00–4:20pm.

Course staff

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

Course Assistant: Yunda Zhong
Office: Ryerson N375
Office hours: TBA

Problem Sets (Part I)

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)

Supplementary materials


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