Mathematics of Data: Optimization Basics

This lecture covers some basics on optimization, including linear algebra concepts such as norms, matrix norms, dual norms, and semi-norms. It also delves into the topics of continuity, Lipschitz continuity, differentiation, convexity, and convergence rates. The lecture introduces the concept of L-Lipschitz gradient functions and strong convexity. Various examples and definitions are provided to illustrate these mathematical concepts.