"Lec 36 - 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_play_list?p=3940DD956CDF0622 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
Video is embedded from external source so embedding is not available.
Video is embedded from external source so download is not available.
Tags: Math Technology Algebra calculus geometry electrical engineering convex optimization subgradient derivatives basic inequality functions algorithms causal state-feedback control stochastic finite dynamic certainty equival
Duration: 77m 4s
No content is added to this lecture.
This video is a part of a lecture series from of stanford