Convex Optimization I
JUNE 22, 2026 — AUGUST 13, 2026
EE364A
Details:
Time: T, R 12:00 PM - 2:00 PM
Units: 3
Class Number: 11492
Interest Area: Computer Science and Engineering
Population: Undergraduate, Graduate
Interest Area: Computer Science and Engineering
Course Format & Length: In Person, 8 Weeks
Pre-requisites:
Pre-requisites
We expect visiting students to have knowledge that is equivalent to the listed Stanford pre-requisite course.
Linear algebra such as EE 263, basic probability.
Cross Listing: CME364A CONVEX OPTIMIZATION I
Grading Basis: Letter or Credit/No Credit
Description:
Convex sets, functions, and optimization problems. The basics of convex analysis and theory of convex programming: optimality conditions, duality theory, theorems of alternative, and applications. Least-squares, linear and quadratic programs, semidefinite programming, and geometric programming. Numerical algorithms for smooth and equality constrained problems; interior-point methods for inequality constrained problems. Applications to signal processing, communications, control, analog and digital circuit design, computational geometry, statistics, machine learning, and mechanical engineering.
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