
This course is about convex optimization. The image on the left illustrates how we can build a ‘‘sparse graphical model’’ based on Senate voting data, revealing an inner structure of the two political parties. The graph is obtained using a convex approximation described here.
The course covers the following topics.
Convex optimization: convexity, duality.
Algorithms: emphasis on firstorder, largescale methods.
Distributed optimization.
Selected topics: robustness, algebraic geometry.

Notes:
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 taking EE 127, or its graduatelevel version EE 227AT (offered concurrently).
