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- ∇fThe gradient (or gradient vector field) of a scalar function f(x1, x2, x3, …, xn) is denoted ∇f or ∇→f where ∇ (nabla) denotes the vector differential operator, del. The notation grad f is also commonly used to represent the gradient.en.wikipedia.org/wiki/Gradient
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The gradient (or gradient vector field) of a scalar function f(x1, x2, x3, …, xn) is denoted ∇f or ∇→f where ∇ (nabla) denotes the vector differential operator, del. The notation grad f is also commonly used to represent the gradient. The gradient of f is defined as the unique vector field whose dot product with any … See more
In vector calculus, the gradient of a scalar-valued differentiable function $${\displaystyle f}$$ of several variables is the vector field (or vector-valued function) See more
Relationship with total derivative
The gradient is closely related to the total derivative (total differential) $${\displaystyle df}$$: … See moreJacobian
The Jacobian matrix is the generalization of the gradient for vector-valued functions of several variables and See more• Korn, Theresa M.; Korn, Granino Arthur (2000). Mathematical Handbook for Scientists and Engineers: Definitions, Theorems, and Formulas for Reference and Review. Dover … See more
Consider a room where the temperature is given by a scalar field, T, so at each point (x, y, z) the temperature is T(x, y, z), independent of time. … See more
Level sets
A level surface, or isosurface, is the set of all points where some function has a given value.
If f is differentiable, … See more• Curl – Circulation density in a vector field
• Divergence – Vector operator in vector calculus
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WebLearn what the gradient of a function is, how to calculate it, and how to use it to find directional derivatives. The symbol for gradient is ∇, named nabla, and it is a vector-valued function of partial derivatives.
Nabla Symbol (∇)
WebLearn how to calculate the gradient of a scalar field, a vector that points in the direction of greatest increase of the field. See the formula, the geometric meaning, and an example of finding the gradient.
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