EE236B - Convex Optimization (Winter Quarter 2014-15)

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


Exercise numbers with prefix ’T’ refer to the textbook. Exercise numbers with prefix ’A’ refer to the collection of additional exercises.

Homework is due at 4PM on the due date. It can be submitted in the 236B homework box in the TA meeting room (67-112 Engineering 4).

Homework solutions and grades are posted on the EEweb course website. (Follow the links to “Assignments” or “Grades”.)

Course information

Lectures: Boelter 5440, Tuesday & Thursday 10:00-11:50AM.

Textbook The textbook is Convex Optimization, available online and in hard copy at the UCLA bookstore. The following books are useful as reference texts.

Course requirements. Weekly homework assignments; open-book final exam on Monday, March 16, 8:00-11:00 AM. The weights in the final grade are: homework 20%, final exam 80%.

Software. We will use CVX, a MATLAB software package for convex optimization. Python users are welcome to use CVXPY instead of MATLAB and CVX.