Optimization Basics: Linear Algebra, Analysis, Convexity

This lecture covers the basics of optimization, including linear algebra concepts such as norms and matrix norms, analysis topics like continuity and Lipschitz continuity, and convexity principles such as convex sets and functions. It also delves into subdifferentials, L-Lipschitz gradient functions, and strong convexity. The lecture further explores convergence rates, convergence plots, and outlines the upcoming topic of gradient descent methods.