Spring 2025

Course schedule: Monday, Wednesday 1:30 PM - 2:50 PM at Shriram 104

Instructor: Mert Pilanci, pilanci@stanford.edu

Instructor Office Hours: see Canvas page

TAs: Sungyoon Kim (sykim777@stanford.edu)

Emi Zeger (emizeger@stanford.edu )

Aaron Mishkin (mishkin@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. 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