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: 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 2027):
EE 227BT – MoWe 17:00-18:29, Cory 247 – Venkatachalam Anantharam

Class Notes
Phase 1 and 2 seats are open to EECS Grad students. Remaining seats open during the adjustment period.

**Enrollment Permission seats are reserved for internal programs and are not open. Please DO NOT email the instructor or scheduling to request a seat**

Links:

• EECS Course Archives (CalNet authentication required)
• CS enrollment policies
• EE enrollment policies
• HKN course maps