Spring 2026

Course schedule: Monday, Wednesday 1:30 PM - 2:50 PM at CODA B90

Instructor: Mert Pilanci, pilanci@stanford.edu

Instructor Office Hours: see Canvas page

TA: Sungyoon Kim (sykim777@stanford.edu)

office hours: see Canvas page

Units: 3

Grading:

HW 60%, Project (or Final) 40%

Course description

Continuation of 364A. Subgradient, cutting-plane, and ellipsoid methods. Decentralized convex optimization via primal and dual decomposition. Monotone operators and proximal methods; alternating direction method of multipliers. Exploiting problem structure in implementation. Convex relaxations of hard problems. Global optimization via branch and bound. Robust and stochastic optimization. Modern non-convex optimizers. Applications in areas such as control, circuit design, signal processing, machine learning and communications. Course requirements include a final project.

Prerequisites:

EE364a - Convex Optimization I