Convex Optimization II

Stanford, , Prof. Stephen Boyd

Updated On 02 Feb, 19


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 and bound. Robust optimization. Selected applications in areas such as control, circuit design, signal processing, and communications.