SignUpConvex Optimization II
Stanford, , Prof. Stephen Boyd
Updated On 02 Feb, 19
x
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.
4.1 ( 11 )
Sam
Sep 12, 2018
Excellent course helped me understand topic that i couldn't while attendinfg my college.
Dembe
March 29, 2019
Great course. Thank you very much.
