EE364B - Convex Optimization II

Convex Optimization II continues Stephen Boyd's EE364A and is published in full on Stanford Engineering Everywhere. Where the first course builds the theory, this one covers the methods needed for large or awkward problems: subgradient, cutting-plane and ellipsoid methods, decentralised optimization via primal and dual decomposition, alternating projections, exploiting problem structure in implementation, convex relaxations of hard problems and global optimization via branch and bound, robust optimization, and selected applications in control, circuit design, signal processing and communications. The page lists Convex Optimization I as the prerequisite and notes that the campus course required a substantial project.

SEE provides 18 lecture videos with transcripts and, for each lecture topic, slides, notes and Matlab or Python files, plus additional notes on relaxation methods for non-convex QCQPs, convex-concave games and numerical linear algebra software, along with assignments and resource links. Access is free without registration under a CC BY-NC-SA 4.0 licence; there is no credit, certificate or instructor feedback.