EE 698W: Convex Optimization in SP/COM

1. Instructors: Ketan Rajawat, Aditya Jagannatham
2. Units: 3-0-0-4
3. Prerequisites: EE605+EE621 OR Instructor Consent 
4. Objective: Convex optimization has recently been applied to a wide variety of problems in EE, especially in signal processing, communications, and networks. The aim of this course is to train the students in application and analysis of convex optimization problems in signal processing and wireless communications. At the end of this course, the students are expected to:
1. Know about the applications of convex optimization in signal processing, wireless communications, and networking research.
2. Be able to recognize convex optimization problems arising in these areas.
3. Be able to recognize ‘hidden’ convexity in many seemingly non-convex problems; formulate them as convex problems. 
4. Be able to develop low-complexity, approximate solutions for difficult non-convex problems.
5. References:
1. Stephen Boyd and Lieven Vandenberghe, Convex Optimization, Cambridge University Press. [Online]. http://www.stanford.edu/~boyd/cvxbook/
2. IEEE Signal Processing Magazine- Special Issue on Advances in Convex Optimization, Vol. 27, No. 3, May 2010.
3. Dimitri P. Bertsekas, Convex Analysis and Optimization, Athena-Scientific, 2003.
6. Format (tentative)
1. Major quiz (20) on 28-01-13, covers: mathematical background, convex sets, functions, and problems (lectures 1-10).
2. Mid-sem exam (20) on 19-02-13, 17:30-19:30
3. End-sem exam (25) on 22-04-13, 9:00-12:00, L3
4. 2 Assignments (5x2) handed out on 7-1 (due 16-1), 16-1 (due 23-1), due coming Monday, 11am.
5. 1 Computer Assignemnts (10) handed out on 28-1 (due 11-2)
6. 1 Term paper (15), papers due March 22 (2), final submission due April 15 (13)
7. Time and place: MWF 11-12, TB 201 
8. Course material:
1. Assignment 1
2. Proof that operator norm is a norm
3. Assignment 2 Solution: LHC notice board.
4. Computer Assignment 1
5. Assignment 3 [submission not required]