Numerical gradient

returns
the one-dimensional numerical
gradient of vector `FX`

= gradient(`F`

)`F`

. The output `FX`

corresponds
to ∂*F*/∂*x*, which
are the differences in the *x* (horizontal) direction.
The spacing between points is assumed to be `1`

.

`[`

returns the `FX`

,`FY`

]
= gradient(`F`

)*x* and *y* components
of the two-dimensional numerical
gradient of matrix `F`

. The additional output `FY`

corresponds
to ∂*F*/∂*y*, which
are the differences in the *y* (vertical) direction.
The spacing between points in each direction is assumed to be `1`

.

Use

`diff`

or a custom algorithm to compute multiple numerical derivatives, rather than calling`gradient`

multiple times.

`gradient`

calculates the *central
difference* for interior data points. For example, consider
a matrix with unit-spaced data, `A`

, that has horizontal
gradient `G = gradient(A)`

. The interior gradient
values, `G(:,j)`

, are

G(:,j) = 0.5*(A(:,j+1) - A(:,j-1));

The subscript `j`

varies between `2`

and `N-1`

,
with `N = size(A,2)`

.

`gradient`

calculates values along the edges
of the matrix with *single-sided differences*:

G(:,1) = A(:,2) - A(:,1); G(:,N) = A(:,N) - A(:,N-1);

If you specify the point spacing, then `gradient`

scales
the differences appropriately. If you specify two or more outputs,
then the function also calculates differences along other dimensions
in a similar manner. Unlike the `diff`

function, `gradient`

returns
an array with the same number of elements as the input.