This course concentrates on recognizing and solving convex optimization problems that arise in engineering.
Applications to signal processing, control, digital and analog circuit design, computational geometry, statistics, and mechanical engineering.
There are two parts:
- Part I. Basics of convex analysis, including least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems.
- Part II. Extends Part I, covering topics that include subgradient, cutting-plane, and ellipsoid methods, plus decentralized convex optimization via primal and dual decomposition.
Stanford: Convex optimization I.
Stanford: Convex optimization II.
