The University of Texas at Austin
Department of Electrical and Computer Engineering

EE381K - Large Scale Convex Optimization

Fall Semester 2015

Some Basic Information

Instructors: Constantine Caramanis

Email: constantine AT utexas DOT edu
Phone: (512) 471-9269
Office: UTA 7.206
Constantine's Office Hours: TBA

TA: Craig Corcoran

Email: ccor5588 AT gmail DOT com
Office: TBA
Office Hours: TBA


Time: Tuesday and Thursday, 12:30-2:00 PM,
Location: CPE 2.212

Course Overview

This course will focus on Convex Optimization including basic material from convex geometry, convex analysis and convex optimization. It will cover basic modeling, and understanding how to find and exploit convexity, both for theoretical analysis, and also for developing algorithms. Understanding algorithms for large scale convex optimization will be a major focus of this course. One major source of motivation for us, will be problems from large scale Machine Learning problems.

Intended audience: This class is structured to be interesting and relevant to students who are using or plan to use optimization in their research, and are interested in solving large-scale optimization problems. The target audience is quite broad: graduate students from ECE, CS, OR, Math, DSSC, and related disciplines.

Course Prerequisites

This class does not assume previous exposure to optimization. (However, if you have previously taken courses such as Linear Programming, there should be minimal overlap with this class.) Good familiarity with Liner Algebra (at the level of, e.g., EE380K) is important, as we will freely use concepts, tools and techniques from linear algebra. Some basic knowledge of Matlab will also be needed, although basic familiarity with programming should be sufficient.

General Note: If you are concerned about the prerequisites or your background, or what the course will cover, please don't hesitate to contact us by e-mail, or come by office hours.

Grading: Homeworks, Exams and Scribing

The grade for the course will be determined by three components: Problem sets (15%), Scribing (5%), a Midterm Exam (35%) and a Final Exam (45%). Problem sets will be given out approximately weekly. The midterm and final will be in-class closed-book exams.

Scribing: TBD

Policy on Collaboration: Discussion of homework questions is encouraged. Please be sure to submit your own independent homework solution. This includes any matlab code required for the assignments. Late homework assignments will not be accepted.

Text and References

The course will be taught from a collection of sources. The primary reference is the book: Convex Optimization by Stephen Boyd and Lieven Vandenberghe. Note that this book is available on line.

Additional References: Some additional references that may be helpful: