EE364b - Course Information

Spring 2020

Course schedule: Mon, Wed 3:00 PM - 4:20 PM

Location: Online via Zoom. Please see Canvas homepage for details.

Instructor: Mert Pilanci, pilanci@stanford.edu

Instructor Office Hours: Online via Zoom, Monday 4:30-5:30pm

TAs: Jonathan Lacotte, lacotte@stanford.edu Tolga Ergen, ergen@stanford.edu

office hours: Online via Zoom, Tuesdays and Thursdays, 10:00 AM - 11:00 AM. Please see the Canvas homepage for Zoom links

Units: 3

Grading:

HW 50%, Project/Final 50%

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 project or a final exam.

Prerequisites:

EE364a - Convex Optimization I