E0 265: Convex Optimization and Applications

Announcements:

  • If you wish to credit/audit the course, please fill this form before Feb 24: https://tinyurl.com/4p4qa7d9.

  • The first MS Teams meeting is on Feb 25 at 10am. (I will add you to MS Teams once you have filled in the above form).


Course Details

  • Term: February - May 2021.

  • Credits: 3:1

  • Hours: Tuesday and Thursday (10:00 - 11:30 hrs).

  • Venue: Online using MS Teams. You should fill up this form (https://tinyurl.com/4p4qa7d9) if you wish to register.

  • Instructor: Kunal Chaudhury.

  • Prerequisites: linear algebra, differential calculus, real analysis.

  • Topics: convex sets and functions, characterizations of convexity, topological properties, separation theorems, subdifferential calculus, optimality conditions, iterative algorithms, monotone operator and fixed point theory, convergence analysis.

*The focus will be on theory and the analysis of iterative algorithms, and not so much on applications.

  • Grading: Mid-terms: 50%; final exam: 50%.

  • References:

  1. Convexity and Optimization in Rn by Berkovitz, 2002.

  2. Convex Analysis by Rockafellar, 1970.

  3. Lectures on Convex Optimization by Nesterov, 2018.

  4. Primer on Monotone Operator Methods by Ryu and Boyd, 2016.

  5. Proximal Algorithms by Parikh and Boyd, 2014.