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In Vector Calculus , the gradient of a Scalar Field is a Vector Field which points in the direction of the greatest rate of increase of the scalar field, and whose Magnitude is the greatest rate of change.

A generalization of the gradient, for functions on a Banach Space which have vectorial values, is the Jacobian .


INTERPRETATIONS OF THE GRADIENT

Consider a room in which the temperature is given by a scalar field \phi, so at each point (x,y,z) the temperature is \phi(x,y,z). We will assume that the temperature does not change in time. Then, at each point in the room, the gradient at that point will show the direction in which the temperature rises most quickly. The magnitude of the gradient will determine how fast the temperature rises in that direction.

Consider a hill 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 . Consider again the example with the hill and suppose that the steepest slope on the 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 with the uphill direction (the gradient vector), 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. The gradient of the hill height function H Dotted with a unit Vector gives the slope of the hill in the direction of the vector. This is called the Directional Derivative .


FORMAL DEFINITION

The gradient (or gradient vector field) of a scalar function f(x) with respect to a vector variable x = (x_1,\dots,x_n) is denoted by
abla f or ec{
abla} f where
abla (the Nabla Symbol ) denotes the vector Differential Operator Del . The notation \operatorname{grad}(f) is also used for the gradient.

By definition, the gradient is a Vector Field whose components are the Partial Derivatives of f. That is:
:
abla f = \left( rac{\partial f}{\partial x_1 }, \dots, rac{\partial f}{\partial x_n } ight).
(Here the gradient is written as a row vector, but it is often taken to be a column vector.)

The Dot Product (
abla f)_x\cdot 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 the gradient of ''f'' is Orthogonal to the Level Set s of ''f''. This also shows that, although the gradient was defined in terms of coordinates, it is actually invariant under Orthogonal Transformation s, as it should be, in view of the geometric interpretation given above.

The gradient is an Irrotational Vector Field and line integrals through a gradient field are path independent and can be evaluated with the Gradient Theorem . Conversely, an irrotational vector field in a Simply Connected region is always the gradient of a function.


EXPRESSIONS FOR THE GRADIENT IN 3 DIMENSIONS

The form of the gradient depends on the coordinate system used.

In Cartesian Coordinates , the above expression expands to


abla f(x, y, z) = \begin{pmatrix}
{ rac{\partial f}{\partial x}},
{ rac{\partial f}{\partial y}},
{ rac{\partial f}{\partial z}}
\end{pmatrix}.

In Cylindrical Coordinates ,


abla f( ho, heta, z) = \begin{pmatrix}
{ rac{\partial f}{\partial ho}},
{ rac{1}{ ho} rac{\partial f}{\partial heta}},
{ rac{\partial f}{\partial z}}
\end{pmatrix}

(where heta is the azimuthal angle and z is the axial coordinate).

In Spherical Coordinates :


abla f(r, \phi, heta) = \begin{pmatrix}
{ rac{\partial f}{\partial r}},
{ rac{1}{r} rac{\partial f}{\partial \phi}},
{ rac{1}{r \sin\phi} rac{\partial f}{\partial heta}}
\end{pmatrix}

(where \phi is the polar angle and heta is the azimuthal angle).


Example

For example, the gradient of the function in Cartesian coordinates
: f(x,y,z)= \ 2x+3y^2-\sin(z)
is:
:
abla f= \begin{pmatrix}
{ rac{\partial f}{\partial x}},
{ rac{\partial f}{\partial y}},
{ rac{\partial f}{\partial z}}
\end{pmatrix} =
\begin{pmatrix}
{2},
{6y},
{-\cos(z)}
\end{pmatrix}.



THE GRADIENT AND THE DERIVATIVE OR DIFFERENTIAL


Linear approximation to a function

The gradient of a Function f from the Euclidean Space \mathbb{R}^n to \mathbb{R} at any particular point ''x''0 in \mathbb{R}^n characterizes the best Linear Approximation to ''f'' at ''x''0. The approximation is as follows:
: f(x) \approx f(x_0) + (
abla f)_{x_0}\cdot(x-x_0)
for x close to x_0, where (
abla f)_{x_0} is the gradient of ''f'' computed at x_0, and the dot denotes the Dot Product on \mathbb{R}^n. This equation is equivalent to the first two terms in the multi-variable Taylor Series expansion of ''f'' at ''x''0.


The differential or (exterior) derivative


The best linear approximation to a function f : \mathbb{R}^n o \mathbb{R} at a point x in \mathbb{R}^n is a linear map from \mathbb{R}^n to \mathbb{R} which is often denoted by \mathrm{d}f_x 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
: (
abla f)_x\cdot v = \mathrm d f_x(v)
for any v \in \mathbb{R}^n. The function \mathrm{d}f, which maps x to \mathrm{d}f_x, is called the differential or Exterior Derivative of f and is an example of a Differential 1-form .

If \mathbb{R}^n is viewed as the space of (length n) column vectors (of real numbers), then one can regard \mathrm{d}f as the row vector
: \mathrm{d}f = \left( rac{\partial f}{\partial x_1}, \dots, rac{\partial f}{\partial x_n} ight)
so that \mathrm{d}f_x(v) is given by matrix multiplication. The gradient is then the corresponding column vector, i.e.,
abla f = \mathrm{d} f^T.


The covariance of the gradient


The differential is more natural than the gradient because it is invariant under all coordinate transformations (or Diffeomorphism s), whereas the gradient is only invariant under orthogonal transformations (because of the implicit use of the dot product in its definition). Because of this, it is common to blur the distinction between the two concepts using the notion of Covariant And Contravariant Vectors . From this point of view, the components of the gradient transform covariantly under changes of coordinates, so it is called a covariant vector field, whereas the components of a vector field in the usual sense transform contravariantly. In this language the gradient ''is'' the differential, as a covariant vector field is the same thing as a differential 1-form.