"Lec 28 - Convex Optimization II (Stanford)" Lecture by Professor Stephen Boyd for Convex Optimization II (EE 364B) in the Stanford Electrical Engineering department. Professor Boyd concludes his lecture on primal and dual decomposition 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. EE 364B Course Website: http://www.stanford.edu/class/EE364B/courseinfo.html Stanford University: http://www.stanford.edu/ Stanford on YouTube: http://www.youtube.com/stanford
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Tags: Math Technology Algebra calculus geometry electrical engineering convex optimization subgradient derivatives basic inequality function algorithms separable problems complicating variables primal decomposition dual con
Duration: 70m 12s
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