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In mathematics, the gradient is a generalization of the usual concept of derivative to the functions of several variables. If f(x1, ..., xn) is a differentiable function of several variables, also called "scalar field", its gradient is the vector of the n partial derivatives of f. It is thus a vector-valued function also called vector field.
Similarly to the usual derivative, the gradient represents the slope of and the tangent of the graph of the function. More precisely, the gradient points in the direction of the greatest rate of increase of the function and its magnitude is the slope of the graph in that direction. The components of the gradient are the non-constant coefficients of the equation of the tangent space to the graph.
Let f be differentiable function defined on a Euclidean space. It becomes a differentiable function of several variables as soon as one chooses an orthonormal frame. The gradient does not depend of the choice of this orthonormal frame. It follows that one may speak of the gradient of f without choosing explicitly a frame.
The Jacobian is the generalization of the gradient for vector valued functions of several variables and differentiable maps between Euclidean spaces or, more generally, manifolds. A further generalization for a function between Banach spaces is the Fréchet derivative.
Consider a room in which the temperature is given by a scalar field, T, so at each point (x,y,z) the temperature is T(x,y,z). (We will assume that the temperature does not change over time.) At each point in the room, the gradient of T at that point will show the direction the temperature rises most quickly. The magnitude of the gradient will determine how fast the temperature rises in that direction.
Consider a surface whose height above sea level at a point (x, y) is H(x, y). The gradient of H at a point is a 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%. If a road goes directly up the hill, then the steepest slope on the road will also be 40%. If, instead, the road goes around the hill at an angle, then it will have a shallower slope. For example, if the angle between the road and the uphill direction, projected onto the horizontal plane, is 60°, then the steepest slope along the road will be 20%, which is 40% times the cosine of 60°.
This observation can be mathematically stated as follows. 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. More precisely, when H is differentiable, the dot product of the gradient of H with a given unit vector is equal to the directional derivative of H in the direction of that unit vector.
The gradient (or gradient vector field) of a scalar function f(x1, x2, x3, ..., xn) is denoted ∇f or where ∇ (the nabla symbol) denotes the vector differential operator, del. The notation "grad(f)" is also commonly used for 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,
In a rectangular coordinate system, the gradient is the vector field whose components are the partial derivatives of f:
where the ei are the orthogonal unit vectors pointing in the coordinate directions. When a function also depends on a parameter such as time, the gradient often refers simply to the vector of its spatial derivatives only.
In the three-dimensional Cartesian coordinate system, this is given by
for x close to x0, where 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 multi-variable Taylor Series expansion of f at x0.
The best linear approximation to a function
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 gradient is therefore related to the differential by the formula
If ℝn is viewed as the space of (length 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. The gradient is then the corresponding column vector, i.e.,
where ⋅ is the dot product.
As a consequence, the usual properties of the derivative hold for the gradient:
The gradient is linear in the sense that if f and g are two real-valued functions differentiable at the point a ∈ Rn, and α and β are two constants, then αf + βg is differentiable at a, and moreover
If f and g are real-valued functions differentiable at a point a ∈ Rn, then the product rule asserts that the product (fg)(x) = f(x)g(x) of the functions f and g is differentiable at a, and
Suppose that f : A → R is a real-valued function defined on a subset A of Rn, and that f is differentiable at a point a. There are two forms of the chain rule applying to the gradient. First, suppose that the function g is a parametric curve; that is, a function g : I → Rn maps a subset I ⊂ R into Rn. If g is differentiable at a point c ∈ I such that g(c) = a, then
where ∘ is the composition operator. More generally, if instead I ⊂ Rk, then the following holds:
where (Dg)T denotes the transpose Jacobian matrix.
For the second form of the chain rule, suppose that h : I → R 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)x ⋅ v 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.
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.
where gx( , ) denotes the inner product of tangent vectors at x defined by the metric g and ∂Xf (sometimes denoted X(f)) is the function that takes any point x ∈ M 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:
where Xj denotes the jth component of X in this coordinate chart.
So, the local form of the gradient takes the form:
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.
where ϕ is the azimuthal angle, z is the axial coordinate, and eρ, eφ and ez are unit vectors pointing along the coordinate directions.
For the gradient in other orthogonal coordinate systems, see Orthogonal coordinates (Differential operators in three dimensions).
In rectangular coordinates, the gradient of a vector f = (f1, f2, f3) is defined by
or the Jacobian matrix
In curvilinear coordinates, the gradient involves Christoffel symbols.
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