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.
What you'll learn
understanding convex sets and functions
formulating and solving least-squares and quadratic programming problems
applying interior-point methods
utilizing duality theory in optimization
Course objectives
recognize and classify convex optimization problems
solve practical optimization challenges in diverse fields
analyze optimality conditions and their implications