EE364b: Lecture Slides and Notes

Instructor: Mert Pilanci (originally developed by Stephen Boyd), Stanford University

These slides and notes will change and get updated throughout the quarter. Please check this page frequently. Unlike EE364a, where the lectures proceed linearly, the lectures for EE364b fall into natural groups, and there is much more freedom as to the order in which they are covered. We are unlikely to cover all of these topics in lecture.

Subgradient methods

Localization methods

Decomposition and distributed optimization

Proximal and operator splitting methods

Self-concordance and Interior Point Method

Conjugate gradients

Nonconvex problems

Neural networks

Monte Carlo sampling methods

Additional lecture notes