EE227BT: Convex Optimization  —  Fall 2013

Instructor: Laurent El Ghaoui
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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 first-order, large-scale methods.

  • Distributed optimization.

  • Selected topics: robustness, algebraic geometry.


  1. 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…

  2. This is not an entry-level graduate class. If you never took an introductory graduate class in optimization, I strongly recommend taking EE 127, or its graduate-level version EE 227AT (offered concurrently).

  • Lectures: Tu,Th 11-1230P, 521 CORY.

  • Discussion section: TBA.