EE 732 - Convex Optimization and Engineering Applications

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Lecture notes

Acknowledgement: All the handouts are available from the course web of EE236B taught by Prof. Lieven Vandenberghe, UCLA. The instructor would like to thank Prof. Vandenberghe for allowing this class to use his 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

Course Information

Lectures:EE 404, Mon Wed 8:00-9:30 AM
Instructor:Jitkomut Songsiri (JSS)
Textbook:We use Convex Optimization as a main reference book for this course.
    1. Boyd and L. Vandenberghe, Convex Optimization, Oxford, 2004
Grading:Homework 40% Midterm 35% Final 15% Term paper 10%
Softwares:Most assignments will involve MATLAB programming with optimization toolbox. Students should install CVX which is a MATLAB package for solving convex programs.