EE364B: Convex Optimization II (Stanford Univ.). Taught by Professor Stephen Boyd, this course concentrates on recognizing and solving
convex optimization problems that arise in engineering. Continuation of Convex Optimization I. Subgradient, cutting-plane, and ellipsoid methods.
Decentralized convex optimization via primal and dual decomposition. Alternating projections. Exploiting problem structure in implementation.
Convex relaxations of hard problems, and global optimization via branch & bound. Robust optimization. Selected applications in areas such as control,
circuit design, signal processing, and communications. Course requirements include a substantial project.
(from see.stanford.edu)
Lecture 13 - Recap: Conjugate Gradient Method and Krylov Subspace