EE236B - Nonlinear Programming (Winter 2008-09)

Lectures 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

Homework

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

Course information

Lectures: Dodd Hall 78. Tuesday & Thursday 10:00-11:50AM.

Textbook and lecture notes. The lecture notes are posted on this website. The textbook is Convex Optimization, available online and in hard copy at the UCLA bookstore. Some books that can serve as secondary (and entirely optional) reference texts are:

Course requirements. Weekly homework assignments; open-book final exam on Friday, March 20, 11:30-2:30PM.

Grading. Approximate weights in the final grade are: homework 25%, final exam 75%.

Software. We will use CVX, a Matlab software package for convex optimization.