The gradient, represented by the blue arrows, denotes the direction of greatest change of a scalar function. The values of the function are represented in greyscale and increase in value from white (low) to dark (high).

The gradient vector can be interpreted as the "direction and rate of fastest increase". If the gradient of a function is non-zero at a point p, the direction of the gradient is the direction in which the function increases most quickly from p, and the magnitude of the gradient is the rate of increase in that direction, the greatest absolute directional derivative.[2] Further, the gradient is the zero vector at a point if and only if it is a stationary point (where the derivative vanishes). The gradient thus plays a fundamental role in optimization theory, where it is used to maximize a function by gradient ascent.

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. At each point in the room, the gradient of T at that point will show the direction in which the temperature rises most quickly, moving away from (x, y, z). The magnitude of the gradient will determine how fast the temperature rises in that direction.

Consider a surface whose height above sea level at point (x, y) is H(x, y). The gradient of H at a point is a plane vector pointing in the direction of the steepest slope or grade at that point. The steepness of the slope at that point is given by the magnitude of the gradient vector.

The gradient can also be used to measure how a scalar field changes in other directions, rather than just the direction of greatest change, by taking a dot product. Suppose that the steepest slope on a hill is 40%. A road going directly uphill has slope 40%, but a road going around the hill at an angle will have a shallower slope. For example, if the road is at a 60° angle from the uphill direction (when both directions are projected onto the horizontal plane), then the slope along the road will be the dot product between the gradient vector and a unit vector along the road, namely 40% times the cosine of 60°, or 20%.

More generally, if the hill height function H is differentiable, then the gradient of H dotted with a unit vector gives the slope of the hill in the direction of the vector, the directional derivative of H along the unit vector.

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 vector v at each point x is the directional derivative of f along v. That is,

Formally, the gradient is dual to the derivative; see relationship with derivative.

When a function also depends on a parameter such as time, the gradient often refers simply to the vector of its spatial derivatives only (see Spatial gradient).

The magnitude and direction of the gradient vector are independent of the particular coordinate representation.[3][4]

In the three-dimensional Cartesian coordinate system with a Euclidean metric, the gradient, if it exists, is given by:

where i, j, k are the standard unit vectors in the directions of the x, y and z coordinates, respectively. For example, the gradient of the function

In some applications it is customary to represent the gradient as a row vector or column vector of its components in a rectangular coordinate system; this article follows the convention of the gradient being a column vector, while the derivative is a row vector.

In cylindrical coordinates with a Euclidean metric, the gradient is given by:[5]

where ρ is the axial distance, φ is the azimuthal or azimuth angle, z is the axial coordinate, and eρ, eφ and ez are unit vectors pointing along the coordinate directions.

where r is the radial distance, φ is the azimuthal angle and θ is the polar angle, and er, eθ and eφ are again local unit vectors pointing in the coordinate directions (that is, the normalized covariant basis).

We consider general coordinates, which we write as x1, …, xi, …, xn, where n is the number of dimensions of the domain. Here, the upper index refers to the position in the list of the coordinate or component, so x2 refers to the second component—not the quantity x squared. The index variable i refers to an arbitrary element xi. Using Einstein notation, the gradient can then be written as:

The latter expression evaluates to the expressions given above for cylindrical and spherical coordinates.

Computationally, given a tangent vector, the vector can be multiplied by the derivative (as matrices), which is equal to taking the dot product with the gradient:

at a point x in Rn is a linear map from Rn to R which is often denoted by dfx or Df(x) and called the differential or total derivative of f at x. The function df, which maps x to dfx, is called the total differential or exterior derivative of f and is an example of a differential 1-form.

Much as the derivative of a function of a single variable represents the slope of the tangent to the graph of the function,[6] the directional derivative of a function in several variables represents the slope of the tangent hyperplane in the direction of the vector.

If Rn is viewed as the space of (dimension n) column vectors (of real numbers), then one can regard df as the row vector with components

so that dfx(v) is given by matrix multiplication. Assuming the standard Euclidean metric on Rn, the gradient is then the corresponding column vector, that is,

The best linear approximation to a function can be expressed in terms of the gradient, rather than the derivative. The gradient of a function f from the Euclidean space Rn to R at any particular point x0 in Rn characterizes the best linear approximation to f at x0. The approximation is as follows:

for x close to x0, where (∇f )x0 is the gradient of f computed at x0, and the dot denotes the dot product on Rn. This equation is equivalent to the first two terms in the multivariable Taylor series expansion of f at x0.

Let U be an open set in Rn. If the function f : UR is differentiable, then the differential of f is the Fréchet derivative of f. Thus f is a function from U to the space Rn such that

As a consequence, the usual properties of the derivative hold for the gradient, though the gradient is not a derivative itself, but rather dual to the derivative:

For the second form of the chain rule, suppose that h : IR is a real valued function on a subset I of R, and that h is differentiable at the point f(a) ∈ I. Then

A level surface, or isosurface, is the set of all points where some function has a given value.

If f is differentiable, then the dot product (∇f )xv of the gradient at a point x with a vector v gives the directional derivative of f at x in the direction v. It follows that in this case the gradient of f is orthogonal to the level sets of f. For example, a level surface in three-dimensional space is defined by an equation of the form F(x, y, z) = c. The gradient of F is then normal to the surface.

More generally, any embedded hypersurface in a Riemannian manifold can be cut out by an equation of the form F(P) = 0 such that dF is nowhere zero. The gradient of F is then normal to the hypersurface.

Similarly, an affine algebraic hypersurface may be defined by an equation F(x1, ..., xn) = 0, where F is a polynomial. The gradient of F is zero at a singular point of the hypersurface (this is the definition of a singular point). At a non-singular point, it is a nonzero normal vector.

The gradient of a function is called a gradient field. A (continuous) gradient field is always a conservative vector field: its line integral along any path depends only on the endpoints of the path, and can be evaluated by the gradient theorem (the fundamental theorem of calculus for line integrals). Conversely, a (continuous) conservative vector field is always the gradient of a function.

The Jacobian matrix is the generalization of the gradient for vector-valued functions of several variables and differentiable maps between Euclidean spaces or, more generally, manifolds.[7][8] A further generalization for a function between Banach spaces is the Fréchet derivative.

Since the total derivative of a vector field is a linear mapping from vectors to vectors, it is a tensor quantity.

In rectangular coordinates, the gradient of a vector field f = ( f1, f2, f3) is defined by:

(where the Einstein summation notation is used and the tensor product of the vectors ei and ek is a dyadic tensor of type (2,0)). Overall, this expression equals the transpose of the Jacobian matrix:

In curvilinear coordinates, or more generally on a curved manifold, the gradient involves Christoffel symbols:

where gjk are the components of the inverse metric tensor and the ei are the coordinate basis vectors.

Expressed more invariantly, the gradient of a vector field f can be defined by the Levi-Civita connection and metric tensor:[9]

For any smooth function f on a Riemannian manifold (M, g), the gradient of f is the vector field f such that for any vector field X,

where gx( , ) denotes the inner product of tangent vectors at x defined by the metric g and Xf is the function that takes any point xM to the directional derivative of f in the direction X, evaluated at x. In other words, in a coordinate chart φ from an open subset of M to an open subset of Rn, (∂Xf )(x) is given by:

Generalizing the case M = Rn, the gradient of a function is related to its exterior derivative, since

More precisely, the gradient f is the vector field associated to the differential 1-form df using the musical isomorphism

(called "sharp") defined by the metric g. The relation between the exterior derivative and the gradient of a function on Rn is a special case of this in which the metric is the flat metric given by the dot product.