Introduction to Mathematical Optimization

Fundamental methods in nonlinear optimization with emphasis on theory and applications. Topics include unconstrained optimization (first- and second-order optimality conditions), convex sets and convex functions, convex optimization, Karush–Kuhn–Tucker (KKT) conditions, and duality. Applications include machine learning, signal processing, and finance, explored through the implementation of basic optimization algorithms. Prerequisites: MATH 42 and MATH 70.