Gradient
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In
where is the total infinitesimal change in for an infinitesimal displacement , and is seen to be maximal when is in the direction of the gradient . The nabla symbol , written as an upside-down triangle and pronounced "del", denotes the vector differential operator.
When a coordinate system is used in which the basis vectors are not functions of position, the gradient is given by the vector[a] whose components are the partial derivatives of at .[2] That is, for , its gradient is defined at the point in n-dimensional space as the vector[b]
Note that the above definition for gradient is only defined for the function , if it is differentiable at . There can be functions for which partial derivatives exist in every direction but fail to be differentiable.
For example, the function unless at origin where , is not differentiable at the origin as it does not have a well defined tangent plane despite having well defined partial derivatives in every direction at the origin.[3] In this particular example, under rotation of x-y coordinate system, the above formula for gradient fails to transform like a vector (gradient becomes dependent on choice of basis for coordinate system) and also fails to point towards the 'steepest ascent' in some orientations. For differentiable functions where the formula for gradient holds, it can be shown to always transform as a vector under transformation of the basis so as to always point towards the fastest increase.
The gradient is dual to the total derivative : the value of the gradient at a point is a
Motivation
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
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.
Notation
The gradient of a function at point is usually written as . It may also be denoted by any of the following:
- : to emphasize the vector nature of the result.
- and : Written with Einstein notation, where repeated indices (i) are repeated.
Definition
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,
where the right-hand side is the directional derivative and there are many ways to represent it. Formally, the derivative is dual to the gradient; 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.[4][5]
Cartesian coordinates
In the three-dimensional
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
Cylindrical and spherical coordinates
In cylindrical coordinates with a Euclidean metric, the gradient is given by:[6]
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.
In spherical coordinates, the gradient is given by:[6]
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).
For the gradient in other
General coordinates
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:
where and refer to the unnormalized local covariant and contravariant bases respectively, is the inverse metric tensor, and the Einstein summation convention implies summation over i and j.
If the coordinates are orthogonal we can easily express the gradient (and the differential) in terms of the normalized bases, which we refer to as and , using the scale factors (also known as
where we cannot use Einstein notation, since it is impossible to avoid the repetition of more than two indices. Despite the use of upper and lower indices, , , and are neither contravariant nor covariant.
The latter expression evaluates to the expressions given above for cylindrical and spherical coordinates.
Relationship with derivative
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Relationship with total derivative
The gradient is closely related to the
While these both have the same components, they differ in what kind of mathematical object they represent: at each point, the derivative is a
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:
Differential or (exterior) derivative
The best linear approximation to a differentiable function
Much as the derivative of a function of a single variable represents the slope of the tangent to the graph of the function,[7] the directional derivative of a function in several variables represents the slope of the tangent hyperplane in the direction of the vector.
The gradient is related to the differential by the formula
If is viewed as the space of (dimension ) column vectors (of real numbers), then one can regard as the row vector with components
Linear approximation to a function
The best linear approximation to a function can be expressed in terms of the gradient, rather than the derivative. The gradient of a function from the Euclidean space to at any particular point in characterizes the best linear approximation to at . The approximation is as follows:
for close to , where is the gradient of computed at , and the dot denotes the dot product on . This equation is equivalent to the first two terms in the multivariable Taylor series expansion of at .
Relationship with Fréchet derivative
Let U be an open set in Rn. If the function f : U → R 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:
- Linearity
- 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
- Product rule
- If f and g are real-valued functions differentiable at a point a ∈ Rn, then the product rule asserts that the product fg is differentiable at a, and
- Chain rule
- 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, thenwhere ∘ is the composition operator: (f ∘ g)(x) = f(g(x)).
More generally, if instead I ⊂ Rk, then the following holds:
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
Further properties and applications
Level sets
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
More generally, any
Similarly, an
Conservative vector fields and the gradient theorem
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.
Generalizations
Jacobian
The
Suppose f : Rn → Rm is a function such that each of its first-order partial derivatives exist on ℝn. Then the Jacobian matrix of f is defined to be an m×n matrix, denoted by or simply . The (i,j)th entry is . Explicitly
Gradient of a vector field
Since the
In rectangular coordinates, the gradient of a vector field f = ( f1, f2, f3) is defined by:
(where the
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:[10]
where ∇c is the connection.
Riemannian manifolds
For any
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
See also
- Curl – Circulation density in a vector field
- Divergence – Vector operator in vector calculus
- Four-gradient – Four-vector analogue of the gradient operation
- Hessian matrix – (Mathematical) matrix of second derivatives
- Skew gradient
- Spatial gradient – Gradient whose components are spatial derivatives
Notes
- ^ row vectorsrepresent covectors, but the opposite convention is also common.
- ^ Strictly speaking, the gradient is a vector field , and the value of the gradient at a point is a tangent vector in the tangent space at that point, , not a vector in the original space . However, all the tangent spaces can be naturally identified with the original space , so these do not need to be distinguished; see § Definition and relationship with the derivative.
- ^ The value of the gradient at a point can be thought of as a vector in the original space , while the value of the derivative at a point can be thought of as a covector on the original space: a linear map .
- ^ the dot product (the slope of the road around the hill) would be 40% if the degree between the road and the steepest slope is 0°, i.e. when they are completely aligned, and flat when the degree is 90°, i.e. when the road is perpendicular to the steepest slope.
- ^ Informally, "naturally" identified means that this can be done without making any arbitrary choices. This can be formalized with a natural transformation.
References
- ^
- Bachman (2007, p. 77)
- Downing (2010, pp. 316–317)
- Kreyszig (1972, p. 309)
- McGraw-Hill (2007, p. 196)
- Moise (1967, p. 684)
- Protter & Morrey (1970, p. 715)
- Swokowski et al. (1994, pp. 1036, 1038–1039)
- ^
- Bachman (2007, p. 76)
- Beauregard & Fraleigh (1973, p. 84)
- Downing (2010, p. 316)
- Harper (1976, p. 15)
- Kreyszig (1972, p. 307)
- McGraw-Hill (2007, p. 196)
- Moise (1967, p. 683)
- Protter & Morrey (1970, p. 714)
- Swokowski et al. (1994, p. 1038)
- ^ "Non-differentiable functions must have discontinuous partial derivatives - Math Insight". mathinsight.org. Retrieved 2023-10-21.
- ^ Kreyszig (1972, pp. 308–309)
- ^ Stoker (1969, p. 292)
- ^ a b Schey 1992, pp. 139–142.
- ^ Protter & Morrey (1970, pp. 21, 88)
- ^ Beauregard & Fraleigh (1973, pp. 87, 248)
- ^ Kreyszig (1972, pp. 333, 353, 496)
- ^ Dubrovin, Fomenko & Novikov 1991, pp. 348–349.
- Bachman, David (2007), Advanced Calculus Demystified, New York: ISBN 978-0-07-148121-2
- Beauregard, Raymond A.; Fraleigh, John B. (1973), A First Course In Linear Algebra: with Optional Introduction to Groups, Rings, and Fields, Boston: ISBN 0-395-14017-X
- Downing, Douglas, Ph.D. (2010), Barron's E-Z Calculus, New York: ISBN 978-0-7641-4461-5)
{{citation}}
: CS1 maint: multiple names: authors list (link - Dubrovin, B. A.; Fomenko, A. T.; Novikov, S. P. (1991). Modern Geometry—Methods and Applications: Part I: The Geometry of Surfaces, Transformation Groups, and Fields. ISBN 978-0-387-97663-1.
- Harper, Charlie (1976), Introduction to Mathematical Physics, New Jersey: ISBN 0-13-487538-9
- ISBN 0-471-50728-8
- "McGraw Hill Encyclopedia of Science & Technology". McGraw-Hill Encyclopedia of Science & Technology (10th ed.). New York: ISBN 978-0-07-144143-8.
- Moise, Edwin E. (1967), Calculus: Complete, Reading: Addison-Wesley
- Protter, Murray H.; Morrey, Charles B. Jr. (1970), College Calculus with Analytic Geometry (2nd ed.), Reading: LCCN 76087042
- Schey, H. M. (1992). Div, Grad, Curl, and All That (2nd ed.). W. W. Norton. OCLC 25048561.
- Stoker, J. J. (1969), Differential Geometry, New York: ISBN 0-471-82825-4
- Swokowski, Earl W.; Olinick, Michael; Pence, Dennis; Cole, Jeffery A. (1994), Calculus (6th ed.), Boston: PWS Publishing Company, ISBN 0-534-93624-5
Further reading
- OCLC 43864234.
External links
- "Gradient". Khan Academy.
- Kuptsov, L.P. (2001) [1994], "Gradient", Encyclopedia of Mathematics, EMS Press.
- Weisstein, Eric W. "Gradient". MathWorld.