EE364b - Convex Optimization II

Announcements

  • Welcome to EE364b, Spring 2020

  • We will use Zoom for the lectures. Please see Canvas for the Zoom link

Instructor: Mert Pilanci, pilanci@stanford.edu

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