Course: Convex Optimization II
by: Tim Papandreou , Stanford subject: Mathematics
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Course description
Continuation of Convex Optimization I.
Topics include: 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.
