Lecture 4 | 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 subgradient methods for constrained problems. 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

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Uploaded: July 9th, 2008 @ 3:31 pm
Author: stanforduniversity

Length: 19:03
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Tags: algebra algorithms basic calculus constrained constraints convex derivatives dual electrical engineering equality function geometry inequality linear math method negativ optimization subgradient technology

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