
This course is about convex optimization. The image on the left illustrates the geometry of 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:
To communicate, we use bCourses and Piazza.
EE 227BT replaces the class previously offered as EE 227A. In the future EE 227BT will be renamed EE 227B, and will be crosslisted again. The ‘‘T’’ means temporary — UC Berkeley has complicated rules about course numbers…
This is not an entrylevel graduate class. If you never took an introductory graduate class in optimization, I strongly recommend first taking EE 127, or its graduatelevel version EE 227AT (offered concurrently in Spring 2016). In particular, I will expect you to be proficient in linear algebra.
Lectures: Tu,Th 111230P, 3106 Etcheverry.
Discussion section: W 12P, 521 CORY.
