
This course covers convex and robust 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, KKT conditions.
Robust optimization: robust optimization, chance constraints, applications.
Here is the projected outline.

Link to UC Berkeley Schedule of Classes: here.
Notes:
To communicate, we use bCourses.
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, we strongly recommend first taking EECS 227AT. In particular, we expect you to be proficient in linear algebra.
Lectures: Tu,Th 9:3011AM, McCone 141.
Discussion sections: W 1011, 204 Wheeler and W 1112, 108 Wheeler.
