This course is devoted to the fundamental theory and algorithms of
constrained nonlinear optimization problems (NLPs).
The students will learn how to design, analyze, and implement
nonlinear optimization algorithms.
Necessary and sufficient optimality conditions, regularity, stability;
line search and trust region methods, quadratic optimization problems
and active set methods, penalty, augmented Lagrangian and SQP methods;
various application examples