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Linearization and the Taylor Series

Explore how to approximate functions using linearization and Taylor series expansions around a point in vector calculus. Understand gradients, Hessians, and polynomial approximations through practical examples and code implementations using Python. This lesson helps you grasp essential techniques for function approximation applicable in optimization and machine learning.

Linearization

Recall that the derivative df/dxdf/dx of a function f(x)f(x) at a point x0x_0 is given as follows:

Assuming x=x0+hx = x_0+h, the equation above can be rearranged and written as follows:

where xx0<ϵ \vert x -x_0 \vert < \epsilon and where ϵ0\epsilon \rightarrow 0. As can be seen, the derivatives df/dxdf/dx can now be used to approximate any function by a straight line around the point xx. For multivariate, the linearization above can be generalized as follows:

where xf(x0)\nabla_xf(x_0) ...