EE236B - Convex Optimization (Winter Quarter 2016-17)

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 (last revised on 1/17).

Homework is due at 4PM on the due date. It can be submitted at the start of the lecture or in the EE236B homework box in the TA meeting room (67-112 Engineering 4).

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

Course information

Lectures: Kinsey 1200B, Tuesday & Thursday 16:00-17:50PM.

Discussion: Boelter 3400, Friday 15:00-15:50PM.

Office hours

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 Wednesday, March 22, 11:30AM-14:30 PM. 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. Julia users are welcome to use Convex.jl.