EE546, Frontiers in Optimization: Convex Optimization Algorithms
Instructors: Maryam Fazel (UWEE) and
Lin Xiao (Microsoft Research)
Modern large-scale convex optimization algorithms have had an immense impact in areas including machine
learning, signal processing, and engineering design. The objectives of this course are to
- Develop working experience of practical optimization algorithms along with their complexity analysis
- Introduce the methodology of structural convex optimization, and develop the capability of designing customized algorithms by exploiting problem structure
- Expose students to research frontiers in convex optimization and its applications
2. Gradient methods
3. Optimal methods
5. Subgradient methods
Proximal gradient methods:
6. Proximal mapping
7. Proximal gradient methods
Decomposition and coordinate descent:
9. Dual decomposition and dual algorithms
10. Augmented Lagrangian, alternating direction multiplier method
11. Coordinate descent method
12. Stochastic and online optimization
13. Newton's method, self-concordant analysis
14. Interior-point methods
15. Cutting plane methods
There will be two homework sets, focusing on implementation (in Matlab) and insights into algorithms discussed in class.
Click here to submit HW files to the Dropbox.
Here is a helpful Matlab tutorial, including object oriented features: Yagtom
Homework 1: assigned 4/9, due 4/18. (pdf file)
(matlab files). Solutions have been posted on the dropbox (click on HW 1 link).
Homework 2: assigned 4/25, due 5/7. Files are in the Dropbox.
Projects can be done individually or in groups of 2. Project types can be:
Project timeline: proposal due: April 25; presentations: May 21,23,30; final report due: June 4
- novel applications and modeling, solution with standard algorithms
- extensive study of particular algorithms: survey, comparison, extensions
Project proposal: 2 pages, including description of topic and problem to be explored and relevant references. Due 4/25.
Project presentations: Please email us by 5/9 to sign up for a 20 minute time slot (15 min talk, 5 min questions) on
one of these 3 class days: May 21, 23, or 30.
Credit: 3 units
Lectures: Mondays and Wednesdays, 1:30-2:50pm, Savery Hall room 166.
Maryam Fazel, office: Paul Allen Center, Room CSE 230.
Lin Xiao, office: TBD
Instructors' office hourse: Wednesdays 3-5pm (or Mondays by appointment)
Prerequisites: EE 578 (Convex optimization) or Math 516 (Numerical Optimization), or consent of the instructors.
Homeworks: 2 homework sets (mainly algorithm implementation)
Final project: project proposal, in-class presentation, and final report
Grading: homeworks 30%, final project 70%.
The lectures notes are largely based on the following books, and on the lectures notes of
Lieven Vandenberghe for EE236C at UCLA, and Stephen
Boyd for EE364B at Stanford University.