EE364b - Convex Optimization IIInstructor: Mert Pilanci, pilanci@stanford.edu
EE364b is the same as CME364b and was originally developed by Stephen Boyd Announcements
Course descriptionContinuation 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. Convex formulations of neural networks 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 |