Introduction
The one-variable Taylor polynomial has a multivariate twin: near a point, any smooth function of many variables looks like a plane (first order) or a bowl/saddle (second order). These local models explain why gradient descent works, how Newton's method jumps straight to the minimum of a quadratic, and how methods like the Laplace approximation and some second-order optimizers are built.