- 1 Definition
- 2 Notation
- 3 Example
- 4 Properties
- 5 Alternative definitions
- 6 Polynomials and power series
- 7 Forward differences
- 8 Computer Program
- 9 Notes
- 10 See also
Given k+1 data points
The forward divided differences are defined as:
The backward divided differences are defined as:
If the data points are given as a function ƒ,
one sometimes writes
Several notations for the divided difference of the function ƒ on the nodes x0, ..., xn are used:
For the first few values of , this yields:
To make the recursive process more clear, the divided differences can be put in a tabular form:
- From the mean value theorem for divided differences it follows that
The divided difference scheme can be put into an upper triangular matrix. Let .
Then it holds
- 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 from the -th column of each . Denote the matrix of eigenvectors with . Example
- The diagonalization of can be written as
With help of a polynomial function with this can be written as
You can represent partial fractions using the expanded form of divided differences. (This does not simplify computation, but is interesting in itself.) If and are polynomial functions, where and is given in terms of linear factors by , then it follows from partial fraction decomposition that
If limits of the divided differences are accepted, then this connection does also hold, if some of the coincide.
If is a polynomial function with arbitrary degree and it is decomposed by using polynomial division of by , then
The divided differences can be expressed as
If nodes are cumulated, then the numerical computation of the divided differences is inaccurate, because you divide almost two zeros, each of which with a high relative error due to differences of similar values. However we know, that difference quotients approximate the derivative and vice versa:
This approximation can be turned into an identity whenever Taylor's theorem applies.
You can eliminate the odd powers of by expanding the Taylor series at the center between and :
- , that is
The Taylor series or any other representation with function series can in principle be used to approximate divided differences. Taylor series are infinite sums of power functions. The mapping from a function to a divided difference is a linear functional. We can as well apply this functional to the function summands.
Express power notation with an ordinary function:
Regular Taylor series is a weighted sum of power functions:
Taylor series for divided differences:
We know that the first terms vanish, because we have a higher difference order than polynomial order, and in the following term the divided difference is one:
It follows that the Taylor series for the divided difference essentially starts with which is also a simple approximation of the divided difference, according to the mean value theorem for divided differences.
If we would have to compute the divided differences for the power functions in the usual way, we would encounter the same numerical problems that we had when computing the divided difference of . The nice thing is, that there is a simpler way. It holds
If you need to compute a whole divided difference scheme with respect to a Taylor series, see the section about divided differences of power series.
Polynomials and power series
Divided differences of polynomials are particularly interesting, because they can benefit from the Leibniz rule. The matrix with
contains the divided difference scheme for the identity function with respect to the nodes , thus contains the divided differences for the power function with exponent . Consequently you can obtain the divided differences for a polynomial function with respect to the polynomial by applying (more precisely: its corresponding matrix polynomial function ) to the matrix .
Now consider increasing the degree of to infinity, i.e. turn the Taylor polynomial to a Taylor series. Let be a function which corresponds to a power series. You can compute a divided difference scheme by computing the according matrix series applied to . If the nodes are all equal, then is a Jordan block and computation boils down to generalizing a scalar function to a matrix function using Jordan decomposition.
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 data points
the divided differences can be calculated via forward differences defined as
- de Boor, Carl, Divided Differences, Surv. Approx. Theory 1 (2005), 46--69
- Opitz, G. Steigungsmatrizen, Z. Angew. Math. Mech. (1964), 44, T52-T54