CS 7301: Convex Optimization
Fall 2021Course Info
An introduction to convex optimization for Ph.D. students.
Where: ECSN 2.112
When: TR, 11:30am-12:45pm
Instructor: Nicholas Ruozzi
Office Hours: T 12:45pm-2:00pm and by appointment online.
TA: TBD
Office Hours: TBD
Grading: problem sets (100%)
Recommended Prerequisites: Mathematical sophistication, e.g., multivariate calculus, linear algebra, probability and statistics, etc.
Schedule & Lecture Slides
Week | Dates | Topic | Readings |
1 | Aug. 24 & 26 | Introduction & Calculus Review Convex Sets and Functions | Boyd Appendix A Boyd 2.1-2.3, 3.1-3.2 |
2 | Aug. 31 & Sept. 2 | Gradient Descent Convex Optimization | Boyd 9.1-9.3 Boyd 4.1-4.3 |
3 | Sept. 7 & 9 | Projected Gradient Duality and Lagrange Multipliers | Boyd 5 |
4 | Sept. 14 & 16 | Constraint Qualification and KKT Second Order Methods | Boyd 5 | 5 | Sept. 21 & 23 | Second Order Methods ML Applications | Boyd 3.3, 9.5, 10.1-10.2 |
6 | Sept. 28 & 30 | Linear Algebra ReviewPositive Semidefinite Matrices | Boyd A.5 |
7 | Oct. 5 & 7 | Singular Value Decomposition Matrix Factorizations | Boyd A.5.4 CUR Decompositions |
8 | Oct. 12 & 14 | More Matrix Factorizations Alternating Projection | |
10 | Oct. 19 & 21 | Proximal Gradient | |
11 | Oct. 26 & 28 | Submodular Functions | Submodular Function Notes, Ch. 1 - 3 |
12 | Nov. 2 & 4 | Convergence Accelerated Gradient | |
13 | Nov. 9 & 11 | Alternating Directions Method of Multipliers Majorization | ADMM Notes |
Problem Sets
All problem sets will be available on eLearning and are to be turned in there. See the homework guidelines below for homework policies.
Textbooks & References
There is no required textbook, but the following books may serve as useful references for different parts of the course.
- Convex Optimization by Stephen Boyd and Lieven Vandenberghe (online) .
- Convex Optimization Theory by Dimitri P. Bertsekas
Homework Guidelines*
I expect you to try solving each problem set on your own. However, if you get stuck on a problem, I encourage you to collaborate with other students in the class, subject to the following rules:
- You may discuss a problem with any student in this class, and work together on solving it. This can involve brainstorming and verbally discussing the problem, going together through possible solutions, but should not involve one student telling another a complete solution.
- Once you solve the homework, you must write up your solutions on your own, without looking at other people's write-ups or giving your write-up to others.
- In your solution for each problem, you must write down the names of any person with whom you discussed it. This will not affect your grade.
- Do not consult solution manuals or other people's solutions from similar courses - ask the course staff, we are here to help!
UT Dallas Course Policies and Procedures
For a complete list of UTD policies and procedures, see here.
*adpated from David Sontag