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

Also Offered As: ELENG 227BT

Prerequisites: Mathematics 54 and Statistics 2 or equivalents.

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: Student Option

Final Exam Status: No

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Class Schedule (Spring 2026):
EE 227BT – MoWe 15:30-16:59, Cory 540AB – Venkatachalam Anantharam

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