Lecture 3.1: multivariate optimization: initial concepts, easy functions
Completion requirements
Preliminaries to (unconstrained) multivariate optimization, the necessary concepts in \R^n: vector space, scalar product, norm, distance. Picturing functions in \R^n: (epi)graph, (sub)level sets and tomography. First examples: linear functions. Linear multivariate optimization and why it's trivial. Quadratic optimization: the trivial (separable) case.