EE227BT: Convex Optimization  —  Fall 2016

Instructor: Laurent El Ghaoui
positive semidefinite matrices 

This course is about convex optimization. The image on the left illustrates the geometry of $2 times 2$ positive semidefinite matrices, which are a central part of the course.

The course covers the following topics.

  • Convex optimization: convexity, conic optimization, duality.

  • Selected topics: robustness, stochastic programming, applications.

Here is the projected outline.

Link to UC Berkeley Schedule of Classes: here.

Notes:

  1. To communicate, we use bCourses.

  2. EE 227BT replaces the class previously offered as EE 227A. In the future EE 227BT will be renamed EE 227B, and will be cross-listed again. The ‘‘T’’ means temporary — UC Berkeley has complicated rules about course numbers…

  3. This is not an entry-level graduate class. If you never took an introductory graduate class in optimization, I strongly recommend first taking EE 127, or its graduate-level version EE 227AT (offered concurrently in Spring 2017). In particular, I will expect you to be proficient in linear algebra.

  • Lectures: Tu,Th 9:30-11AM, Moffitt Library 145.

  • Discussion sections: Fri 10-11AM and Tu 5-6PM , 521 CORY.