Linear Systems and Optimization: Convex Optimization II

Continuation of Convex Optimization I. 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. Course requirements include a substantial project.

Instructor: Stephen Boyd
Source: Stanford University
License: Creative Commons 3.0
Donations: Donate to Stanford