The finite difference is the discrete analog of the derivative. The finite forward difference of a function
is defined as
(1)
|
and the finite backward difference as
(2)
|
The forward finite difference is implemented in the Wolfram Language as DifferenceDelta[f, i].
If the values are tabulated at spacings , then the notation
(3)
|
is used. The th
forward difference would then be written as
, and similarly, the
th backward difference
as
.
However, when
is viewed as a discretization of the continuous function
, then the finite difference is sometimes written
(4)
| |||
(5)
|
where
denotes convolution and
is the odd impulse pair. The finite difference operator
can therefore be written
(6)
|
An th
power has a constant
th finite difference. For example, take
and make a difference table,
(7)
|
The
column is the constant 6.
Finite difference formulas can be very useful for extrapolating a finite amount of data in an attempt to find the general term. Specifically, if a function is known at only a few discrete values
, 1, 2, ... and it is desired to determine the analytical
form of
,
the following procedure can be used if
is assumed to be a polynomial
function. Denote the
th
value in the sequence of interest by
. Then define
as the forward difference
,
as the second forward
difference
,
etc., constructing a table as follows
(8)
| |
(9)
| |
(10)
| |
(11)
|
Continue computing ,
,
etc., until a 0 value is obtained. Then the polynomial
function giving the values
is given by
(12)
| |||
(13)
|
When the notation ,
,
etc., is used, this beautiful equation is called Newton's
forward difference formula. To see a particular example, consider a sequence
with first few values of 1, 19, 143, 607, 1789, 4211, and 8539. The difference table
is then given by
(14)
|
Reading off the first number in each row gives ,
,
,
,
. Plugging these in gives the equation
(15)
| |||
(16)
|
which indeed fits the original data exactly.
Formulas for the derivatives are given by
(17)
| |||
(18)
| |||
(19)
| |||
(20)
| |||
(21)
| |||
(22)
| |||
(23)
| |||
(24)
| |||
(25)
| |||
(26)
| |||
(27)
|
(Beyer 1987, pp. 449-451; Zwillinger 1995, p. 705).
Formulas for integrals of finite differences
(28)
|
are given by Beyer (1987, pp. 455-456).
Finite differences lead to difference equations, finite analogs of differential equations. In fact, umbral calculus displays many elegant analogs of well-known identities for continuous functions. Common finite difference schemes for partial differential equations include the so-called Crank-Nicolson, Du Fort-Frankel, and Laasonen methods.