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

Prerequisites: MATH 54 and STAT 2.

Formats:
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

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Class Schedule (Fall 2024):
EE 227BT – TuTh 14:00-15:29, Anthro/Art Practice Bldg 155 – Benjamin Recht

Class homepage on inst.eecs

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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.