This is an optimization course developed by Laurent El Ghaoui for the EE127a course at UC Berkeley.
It offers an introduction to optimization models, with emphasis on numerically tractable (and practical interesting) problems.
The course starts by introducing basic linear algebra, and then focuses on convex models (Convexity, LP, QP, SOCP, Robust LP, GP, and SDP are all introduces and explained). Non-convex models are also briefly introduced.
An overview on Duality is also given, and the course ends with 4 interesting case studies such as Senate Voting, Antenna Arrays, Localization and Circuit Design.