EE 227BT. Convex Optimization

Catalog Description: Convex optimization is a class of nonlinear optimization problems where the objective to be minimized, and the constraints, are both convex. The course covers some convex optimization theory and algorithms, and describes various applications arising in engineering design, machine learning and statistics, finance, and operations research. The course includes laboratory assignments, which consist of hands-on experiments with the optimization software CVX, and a discussion section.

Units: 4.0

Prerequisites: Mathematics 54 and Statistics 2 or equivalents.

Fall: 3.0 hours of lecture and 1.0 hours of discussion per week
Spring: 3.0 hours of lecture and 1.0 hours of discussion per week

Grading basis: letter

Final exam status: No final exam

Also listed as: EL ENG 227BT

Class Schedule (Fall 2018):
TuTh 11:00AM - 12:29PM, Wurster 102 – Laurent El Ghaoui, Somayeh Sojoudi

Class homepage on inst.eecs

General Catalog listing

Department Notes: This course is about convex optimization. It covers the following topics. Convex optimization: convexity, conic optimization, duality. Selected topics: robustness, stochastic programming, applications.