Gradient Definition: What is the Vector Operator \mathbf\nabla?

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In summary, the gradient is a vector operator represented by the symbol \mathbf\nabla or grad. It is used to find the directional derivative of a scalar function with respect to its variables. In Cartesian, cylindrical, and spherical coordinates, the gradient is calculated using different equations, each involving partial derivatives. The gradient points in the direction of steepest increase of the function and is perpendicular to the level set of the function at a given point. Its properties make it a valuable tool in mathematics and engineering.
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Definition/Summary

The gradient is a vector operator denoted by the symbol [itex]\mathbf\nabla[/itex] or grad. The gradient of a differentiable scalar function [itex]f\left({\mathbf x}\right)[/itex] of a vector [itex]\mathbf{x}=\left(x_1,x_2,\ldots,x_n\right)[/itex] is a vector field whose components are the partial derivatives of [itex]f\left({\mathbf x}\right)[/itex] with respect to the variables [itex]x_1,x_2,\ldots,x_n\,.[/itex] Explicitly,

[tex]\mathbf\nabla f = \left(\frac{\partial f}{\partial x_1}, \frac{\partial f}{\partial x_2}, \ldots, \frac{\partial f}{\partial x_n}\right)[/tex]

Equations

For a function of three variables in Cartesian Coordinates,

[tex]\nabla f\left(x,y,z\right)
= \frac{\partial f}{\partial x}\hat{\mathbf{i}}
+ \frac{\partial f}{\partial y}\hat{\mathbf{j}}
+ \frac{\partial f}{\partial z}\hat{\mathbf{k}}[/tex]


In Cylindrical Polar Coordinates,

[tex]\nabla f\left(r,\theta,z\right)
= \frac{\partial f}{\partial r}\hat{\mathbf{e_r}}
+ \frac{1}{r}\frac{\partial f}{\partial \theta}\hat{\mathbf{e_\theta}}
+ \frac{\partial f}{\partial z}\hat{\mathbf{k}}[/tex]

Where [itex]\hat{\mathbf{e_r}}[/itex] and [itex]\hat{\mathbf{e_\theta}}[/itex] are unit vectors in the radial and angular directions respectively.


In spherical coordinates,

[tex]\nabla f\left(r,\phi,\theta\right)
= \frac{\partial f}{\partial r}\hat{\mathbf{e_r}}
+ \frac{1}{r} \ \frac{\partial f}{\partial \phi} \hat{\mathbf{e_\phi}}
+ \frac{1}{r \ \sin \phi} \ \frac{\partial f}{\partial \theta} \hat{\mathbf{e_\theta}} [/tex]

where [itex]\phi[/itex] is the angle from the +z-axis to the point [itex](r, \phi, \theta ) [/itex]. Also [itex]\hat{\mathbf{e_r}}[/itex], etc., denote unit vectors.

NOTE: this definition of [itex]\phi, \theta[/itex] is the one commonly used in math and engineering textbooks. PHYSICS TEXTBOOKS USUALLY HAVE [itex]\phi, \theta[/itex] DEFINED THE OTHER WAY ROUND.

Extended explanation

The main property of the gradient of [itex]f[/itex], is that it lies in the domain of the function [itex]f[/itex], and points in the direction in which [itex]f[/itex] is increasing fastest. In particular the gradient at a point [itex]\mathbf{p}[/itex] is perpendicular to the "level set" of [itex]f[/itex] through [itex]\mathbf{p}[/itex], where [itex]f[/itex] is constantly equal to [itex]f(\mathbf{p})[/itex].

* This entry is from our old Library feature. If you know who wrote it, please let us know so we can attribute a writer. Thanks!
 
Mathematics news on Phys.org

What is the gradient definition?

The gradient is a mathematical concept that represents the rate of change of a function at a specific point. It is a vector operator denoted by the symbol ∇ (nabla).

What is the vector operator ∇ (nabla)?

The vector operator ∇ (nabla) is a mathematical symbol used to represent the gradient of a function. It is a vector composed of the partial derivatives of a function with respect to its variables.

How is the gradient calculated?

The gradient is calculated by taking the partial derivatives of a function with respect to its variables and arranging them into a vector using the ∇ (nabla) symbol. This vector represents the direction and magnitude of the steepest ascent of the function at a specific point.

What is the significance of the gradient in mathematics?

The gradient is an important concept in mathematics as it allows us to determine the direction and magnitude of the steepest ascent of a function at a specific point. It is also a fundamental concept in vector calculus and is used in various fields such as physics, engineering, and computer science.

Can the gradient be used in higher dimensions?

Yes, the gradient can be used in any number of dimensions. In higher dimensions, the gradient is represented as a vector of partial derivatives in each dimension. This allows us to determine the direction and magnitude of the steepest ascent of a function in multidimensional spaces.

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