This course concentrates on recognizing and solving convex optimization problems that arise in applications.
The syllabus includes:
- Convex sets, functions, and optimization problems.
- Basics of convex analysis.
- Least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems.
- Optimality conditions, duality theory, theorems of alternative, and applications.
- Interior-point methods.
- Applications to signal processing, statistics and machine learning, control and mechanical engineering, digital and analog circuit design, and finance.
EdX: Convex optimization.