Spring

IEORE4009 Non-linear Optimization


Instructor: Donald Goldfarb


A course in linear algebra is a prerequisite.

Textbook: J. Nocedal and S.J. Wright, “Numerical Optimization” (Springer)

1. Convex sets and functions
2. Unconstrained Optimization theory
3. Convex optimization algorithms
4. Conjugate direction and gradient algorithms
5. Newton’s method and modified variants of it
6. Automatic differentiation
7. Line search methods
8. Trust region methods
9. Quasi-Newton methods
10. Constrained optimization theory
11. Constraint qualifications and duality
12. Quadratic programming  and active set methods
13. Penalty, barrier and augmented Lagrangian methods
14. Sequential quadratic programming methods
15. Interior-point methods for NLP
16. Review of optimization software packages



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