E0 330: Convex Optimization and Applications


  • Announcements:

  1. Final exam: 27/04/2015, 10-12 forenoon in EE 303.

  2. Final lecture is on 15/4/2015.

  3. Second mid-term exam: 28/03/2015, 3:00 - 4:30 PM in EE 303.

  4. First mid-term exam: 16/02/2015, 6:00 - 7:30 PM in EE 303.

  5. TA's for CVO: Subhadeep Mukherjee and Anand Sharma.

  6. The first lecture will be on Jan 5 in EE 303.

The above notes are meant to capture the key points of the lecture. Proofs of technical results are generally not included in the notes.

  • Assignments

  1. A1, S1.

  2. A2, S2.

  3. A3, S3.

  4. A4, S4.

Course Details

  • Term: January - April 2015.

  • Credits: 3:1

  • Hours: Monday and Wednesday (9:30 - 11:00 AM).

  • Instructor: Kunal Chaudhury (kunal@ee.iisc.ernet.in).

  • Venue: EE 303.

  • Webpage: http://goo.gl/meuue5

  • Course Description: The focus of the course will be on the fundamental aspects of the subject, both in terms of theory and algorithms. We will also look at various applications of convex optimization in inverse problems, signal processing, image reconstruction, communications, statistics, and machine learning. In the process of understanding various algorithms, the students will be introduced to relevant topics in convex analysis and duality. At the end of the course, the students should be comfortable in framing and solving standard convex optimization problems arising in various scientific and engineering applications.

  • Prerequisites: A course in linear algebra and some programming skills is mandatory. Familiarity with multivariate calculus and real analysis will be helpful. Prior to this course, I strongly suggest taking Computational Methods of Optimization and/or Linear and Non-Linear Optimization, both offered in August-December.

  • Course structure: About 40 hours of instruction, mid-term exams, exercise series (math problems and CVX assignments), and a final exam.

  • Grading: Mid-term: 30%, Exercises: 20%, Final exam: 50%.

  • References:

  1. Boyd and Vandenberghe, Convex Optimization.

  2. Nesterov, Introductory Lectures on Convex Programming.

  3. Bretsekas, Convex Optimization Theory.

  4. Bretsekas, Nonlinear Programming. (see appendix on convex optimization).

  5. Apostol, Mathematical Analysis. (reference for real analysis results).