IE 510 - Applied Nonlinear Programming
Last offered Spring 2022
The course covers the fundamentals of nonlinear optimization. Starting with simple techniques such as bisection and curve fitting, the course builds up to cover more advanced algorithms such as Conjugate Gradient, Newton and Quasi-Newton Methods, Penalty methods and Augmented Lagrangians. KKT conditions and duality theory in nonlinear optimization is covered along as well along with its algorithmic applications. Applications are discussed ranging from engineering systems to statistics and learning theory. Prerequisite: IE 310.