Vector-valued function in the context of "Range of a function"

A light field, or lightfield, is a vector function that describes the amount of light flowing in every direction through every point in a space. The space of all possible light rays is given by the five-dimensional plenoptic function, and the magnitude of each ray is given by its radiance. Michael Faraday was the first to propose that light should be interpreted as a field, much like the magnetic fields on which he had been working. The term light field was coined by Andrey Gershun in a classic 1936 paper on the radiometric properties of light in three-dimensional space.

The term "radiance field" may also be used to refer to similar, or identical concepts. The term is used in modern research such as neural radiance fields.

In mathematics, a zero (also sometimes called a root) of a real-, complex-, or generally vector-valued function ${\displaystyle f}$, is a member ${\displaystyle x}$ of the domain of ${\displaystyle f}$ such that ${\displaystyle f(x)}$ vanishes at ${\displaystyle x}$; that is, the function ${\displaystyle f}$ attains the value of 0 at ${\displaystyle x}$, or equivalently, ${\displaystyle x}$ is a solution to the equation ${\displaystyle f(x)=0}$. A "zero" of a function is thus an input value that produces an output of 0.

A root of a polynomial is a zero of the corresponding polynomial function. The fundamental theorem of algebra shows that any non-zero polynomial has a number of roots at most equal to its degree, and that the number of roots and the degree are equal when one considers the complex roots (or more generally, the roots in an algebraically closed extension) counted with their multiplicities. For example, the polynomial ${\displaystyle f}$ of degree two, defined by ${\displaystyle f(x)=x^{2}-5x+6=(x-2)(x-3)}$ has the two roots (or zeros) that are 2 and 3.$${\displaystyle f(2)=2^{2}-5\times 2+6=0{\text{ and }}f(3)=3^{2}-5\times 3+6=0.}$$

In vector calculus, the gradient of a scalar-valued differentiable function ${\displaystyle f}$ of several variables is the vector field (or vector-valued function) ${\displaystyle \nabla f}$ whose value at a point ${\displaystyle p}$ gives the direction and the rate of fastest increase. The gradient transforms like a vector under change of basis of the space of variables of ${\displaystyle f}$. If the gradient of a function is non-zero at a point ${\displaystyle p}$, the direction of the gradient is the direction in which the function increases most quickly from ${\displaystyle p}$, and the magnitude of the gradient is the rate of increase in that direction, the greatest absolute directional derivative. Further, a point where the gradient is the zero vector is known as a stationary point. The gradient thus plays a fundamental role in optimization theory, where it is used to minimize a function by gradient descent. In coordinate-free terms, the gradient of a function ${\displaystyle f(\mathbf {r} )}$ may be defined by:

$${\displaystyle df=\nabla f\cdot d\mathbf {r} }$$