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In mathematics, Neville's algorithm is an algorithm used for polynomial interpolation that was derived by the mathematician Eric Harold Neville. Given n + 1 points, there is a unique polynomial of degree ≤ n which goes through the given points. Neville's algorithm evaluates this polynomial.

Neville's algorithm is based on the Newton form of the interpolating polynomial and the recursion relation for the divided differences. It is similar to Aitken's algorithm (named after Alexander Aitken), which is nowadays not used.

The algorithm

Given a set of n+1 data points (x_i, y_i) where no two x_i are the same, the interpolating polynomial is the polynomial p of degree at most n with the property

p(x_i) = y_i for all i = 0,…,n

This polynomial exists and it is unique. Neville's algorithm evaluates the polynomial at some point x.

Let p_i,j denote the polynomial of degree j − i which goes through the points (x_k, y_k) for k = i, i + 1, …, j. The p_i,j satisfy the recurrence relation

$p_{i,i}(x)=y_{i},\,$	$0\leq i\leq n,\,$
$p_{i,j}(x)={\frac {(x_{j}-x)p_{i,j-1}(x)+(x-x_{i})p_{i+1,j}(x)}{x_{j}-x_{i}}},\,$	$0\leq i<j\leq n.\,$

This recurrence can calculate p_0,n(x), which is the value being sought. This is Neville's algorithm.

For instance, for n = 4, one can use the recurrence to fill the triangular tableau below from the left to the right.

$p_{0,0}(x)=y_{0}\,$
	$p_{0,1}(x)\,$
$p_{1,1}(x)=y_{1}\,$		$p_{0,2}(x)\,$
	$p_{1,2}(x)\,$		$p_{0,3}(x)\,$
$p_{2,2}(x)=y_{2}\,$		$p_{1,3}(x)\,$		$p_{0,4}(x)\,$
	$p_{2,3}(x)\,$		$p_{1,4}(x)\,$
$p_{3,3}(x)=y_{3}\,$		$p_{2,4}(x)\,$
	$p_{3,4}(x)\,$
$p_{4,4}(x)=y_{4}\,$

This process yields p_0,4(x), the value of the polynomial going through the n + 1 data points (x_i, y_i) at the point x.

This algorithm needs O(n²) floating point operations.

Application to numerical differentiation

Lyness and Moler showed in 1966 that using undetermined coefficients for the polynomials in Neville's algorithm, one can compute the Maclaurin expansion of the final interpolating polynomial, which yields numerical approximations for the derivatives of the function at the origin. While "this process requires more arithmetic operations than is required in finite difference methods", "the choice of points for function evaluation is not restricted in any way". They also show that their method can be applied directly to the solution of linear systems of the Vandermonde type.

References

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• J. N. Lyness and C.B. Moler, Van Der Monde Systems and Numerical Differentiation, Numerishe Mathematik 8 (1966) 458-464

External links

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• Module for Neville Interpolation by John H. Mathews
• Java Code by behzad torkian