Stanford Engineering Everywhere EE364B - 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.

Prerequisites: Convex Optimization I

Course Homepage: [[http://see.stanford.edu/see/courseinfo.aspx?coll=523bbab2-dcc1-4b5a-b78f-4c9dc8c7cf7a]]

Course features at Stanford Engineering Everywhere page: *Convex Optimization II *Lectures *Syllabus *Handouts *Assignments *Exams *Software