Lecture 17 | Convex Optimization II (Stanford)

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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

Published 2008-07-09 · Watch on YouTube ↗
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