In mathematics, divided differences is an algorithm, historically used for computing tables of logarithms and trigonometric functions.[citation needed] Charles Babbage's difference engine, an early mechanical calculator, was designed to use this algorithm in its operation.[1]
Divided differences is a recursive division process. Given a sequence of data points , the method calculates the coefficients of the interpolation polynomial of these points in the Newton form.
Given n + 1 data points
where the are assumed to be pairwise distinct, the forward divided differences are defined as:
To make the recursive process of computation clearer, the divided differences can be put in tabular form, where the columns correspond to the value of j above, and each entry in the table is computed from the difference of the entries to its immediate lower left and to its immediate upper left, divided by a difference of corresponding x-values:
Notation
Note that the divided difference depends on the values and , but the notation hides the dependency on the x-values. If the data points are given by a function f,
one sometimes writes the divided difference in the notation
Other notations for the divided difference of the function ƒ on the nodes x0, ..., xn are:
Divided differences for and the first few values of :
Thus, the table corresponding to these terms upto two columns has the following form:
The divided difference scheme can be put into an upper triangular matrix:
Then it holds
- if is a scalar
- This follows from the Leibniz rule. It means that multiplication of such matrices is commutative. Summarised, the matrices of divided difference schemes with respect to the same set of nodes x form a commutative ring.
- Since is a triangular matrix, its eigenvalues are obviously .
- Let be a Kronecker delta-like function, that is Obviously , thus is an eigenfunction of the pointwise function multiplication. That is is somehow an "eigenmatrix" of : . However, all columns of are multiples of each other, the matrix rank of is 1. So you can compose the matrix of all eigenvectors of from the -th column of each . Denote the matrix of eigenvectors with . Example The diagonalization of can be written as
With the help of the polynomial function this can be written as
If and , the divided differences can be expressed as[4]
where is the -th derivative of the function and is a certain B-spline of degree for the data points , given by the formula
This is a consequence of the Peano kernel theorem; it is called the Peano form of the divided differences and is the Peano kernel for the divided differences, all named after Giuseppe Peano.
Forward and backward differences
When the data points are equidistantly distributed we get the special case called forward differences. They are easier to calculate than the more general divided differences.
Given n+1 data points
with
the forward differences are defined as
whereas the backward differences are defined as:
Thus the forward difference table is written as:whereas the backwards difference table is written as:
The relationship between divided differences and forward differences is[5]
whereas for backward differences:[citation needed]