Concept

# Spline interpolation

Résumé
In the mathematical field of numerical analysis, spline interpolation is a form of interpolation where the interpolant is a special type of piecewise polynomial called a spline. That is, instead of fitting a single, high-degree polynomial to all of the values at once, spline interpolation fits low-degree polynomials to small subsets of the values, for example, fitting nine cubic polynomials between each of the pairs of ten points, instead of fitting a single degree-ten polynomial to all of them. Spline interpolation is often preferred over polynomial interpolation because the interpolation error can be made small even when using low-degree polynomials for the spline. Spline interpolation also avoids the problem of Runge's phenomenon, in which oscillation can occur between points when interpolating using high-degree polynomials. Originally, spline was a term for elastic rulers that were bent to pass through a number of predefined points, or knots. These were used to make technical drawings for shipbuilding and construction by hand, as illustrated in the figure. We wish to model similar kinds of curves using a set of mathematical equations. Assume we have a sequence of knots, through . There will be a cubic polynomial between each successive pair of knots and connecting to both of them, where . So there will be polynomials, with the first polynomial starting at , and the last polynomial ending at . The curvature of any curve is defined as where and are the first and second derivatives of with respect to . To make the spline take a shape that minimizes the bending (under the constraint of passing through all knots), we will define both and to be continuous everywhere, including at the knots. Each successive polynomial must have equal values (which are equal to the y-value of the corresponding datapoint), derivatives, and second derivatives at their joining knots, which is to say that This can only be achieved if polynomials of degree 3 (cubic polynomials) or higher are used. The classical approach is to use polynomials of exactly degree 3 — cubic splines.
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Concepts associés (11)
Interpolation multivariée
En analyse numérique, linterpolation multivariée ou linterpolation spatiale désigne l'interpolation numérique de fonctions de plus d'une variable. Le problème est similaire à celui de l'interpolation polynomiale sur un intervalle réel : on connait les valeurs d'une fonction à interpoler aux points et l'objectif consiste à évaluer la valeur de la fonction en des points . L'interpolation multivariée est notamment utilisée en géostatistique, où elle est utilisée pour reconstruire les valeurs d'une variable régionalisée sur un domaine à partir d'échantillons connus en un nombre limité de points.
Spline cubique d'Hermite
On appelle spline cubique d'Hermite une spline de degré trois, nommée ainsi en hommage à Charles Hermite, permettant de construire un polynôme de degré minimal (le polynôme doit avoir au minimum quatre degrés de liberté et être donc de degré 3) interpolant une fonction en deux points avec ses tangentes. Chaque polynôme se trouve sous la forme suivante : thumb|Les quatre polynômes de base avec ce qui donne le polynôme suivant : Sous cette écriture, il est possible de voir que le polynôme p vérifie : La courbe est déterminée par la position des points et des tangentes.
Spline interpolation
In the mathematical field of numerical analysis, spline interpolation is a form of interpolation where the interpolant is a special type of piecewise polynomial called a spline. That is, instead of fitting a single, high-degree polynomial to all of the values at once, spline interpolation fits low-degree polynomials to small subsets of the values, for example, fitting nine cubic polynomials between each of the pairs of ten points, instead of fitting a single degree-ten polynomial to all of them.