EE364b is the same as CME364b and was originally developed by Stephen Boyd

Announcements

• The first lecture will be on Monday 31, 1:30pm-2:50pm at Shriram 104

• The lectures will be recorded and be available to enrolled students

• Welcome to EE364b Spring 2025!

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. Non-convex heuristics. Convex formulations of neural networks, diffusion models and Monte Carlo sampling. Applications in areas such as control, circuit design, signal processing, machine learning and communications. This class will culminate in a final project.

Prerequisites:

EE364a - Convex Optimization I