Course contents *
Examples of applications and modelling training.
Basic concepts and theory for optimization, in particular theory for convex problems.
Some linear algebra in R^n, in particular bases for the four fundamental subspaces corresponding to a given matrix, and LDLT-factorization of a symmetric definite matrix.
Linear optimization, including duality theory.
Optimization of flows in networks.
Quadratic optimization with linear constraints.
Linear least squares problems, in particular minimum norm solutions.
Unconstrained nonlinear optimization, in particular nonlinear least squares problems.
Optimality conditions for constrained nonlinear optimization, in particular for convex problems.