EE 732 - Convex Optimization and Engineering Applications

Announcement:

• All course information is directed to CourseVille.

Homework

Lecture notes

Acknowledgement: All the handouts are available from the course web of EE236B taught by Prof. Lieven Vandenberghe, UCLA. The instructor would like to thank Prof. Vandenberghe for allowing this class to use his lecture notes.

1. Introduction
2. Convex sets
3. Convex functions
4. Convex optimization problems
5. Duality
6. Approximation and fitting
7. Statistical estimation
8. Geometric problems
9. Numerical linear algebra background
10. Unconstrained minimization
11. Equality constrained minimization
12. Interior-point methods
13. Conclusions

MATLAB files

• data_logistic
• data_robustapprox
• data_tvdenoising
• data_svm_train
• data_svn_validate

Course Information

Lectures:EE 404, Mon Wed 8:00-9:30 AM Instructor:Jitkomut Songsiri (JSS) Textbook:We use Convex Optimization as a main reference book for this course.

• 19. Boyd and L. Vandenberghe, Convex Optimization, Oxford, 2004

Grading:Homework 40% Midterm 35% Final 15% Term paper 10% Softwares:Most assignments will involve MATLAB programming with optimization toolbox. Students should install CVX which is a MATLAB package for solving convex programs.