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Gradients of the Vector-Valued Functions

Explore the concept of gradients for vector-valued functions, extending from scalar outputs to multi-dimensional outputs. Understand how to compute the Jacobian matrix using partial derivatives and apply the chain rule. Gain practical skills in calculating gradients for functions mapping vectors to vectors, supported by Python examples involving NumPy and SciPy.

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 ...