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Gradients of the Vector-Valued Functions

Explore the concept of gradients for vector-valued functions and learn how to compute the Jacobian matrix. Understand how partial derivatives and the chain rule apply to functions mapping vectors to vectors. Gain hands-on experience with Python to calculate gradients using NumPy and SciPy, enabling you to analyze how multidimensional outputs change with respect to their inputs.

Vector-valued functions and their gradients

A vector-valued function in mathematics is a function that takes one or more input variables and produces one or more output variables, where the output variables are components of a vector. For example, the function f(θ)=[cos(θ)sin(θ)]f(\theta) = \begin{bmatrix} \cos(\theta) \\ \sin(\theta) \end{bmatrix} is a vector-valued function that describes a circle in a 2D plane. The function takes input θ[0,2π]\theta \in [0, 2\pi] (the angle with xx-axis) and returns a 2D vector representing the coordinates on a unit circle x=cos(θ)x = \cos(\theta) and y=sin(θ)y = \sin(\theta).

So far, we have discussed the partial derivatives and gradients of functions that take one or more input variables and output a scalar value, i.e., f:RmRf: \R^m \rightarrow \R. Now, we will generalize the notion of gradients for vector-valued functions f:RmRnf: \R^m \rightarrow \R^n, where m1m \geq 1 and n>1n > 1.

Given an mm ...