Lecture 17 | Convex Optimization II (Stanford)
Lecture by Professor Stephen Boyd for Convex Optimization II (EE 364B) in the Stanford Electrical Engineering department. Professor Boyd lectures on Stochastic Model Predictive Control, he then begins discussing Branch-and-bound methods.
This course introduces topics such as 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.
Complete Playlist for the Course:
http://www.youtube.com/view_pl....ay_list?p=3940DD956C
EE364B Course Website:
http://www.stanford.edu/class/ee364b/
Stanford University:
http://www.stanford.edu
Stanford University Channel on YouTube:
http://www.youtube.com/stanford