Cubic spline functions revisited

In this paper a fourth order asymptotically optimal error bound for a new cubic interpolating spline function, denoted by Q-spline, is derived for the case that only function values at given points are used but not any derivative information. The bound seems to be stronger than earlier error bounds for cubic spline interpolation in such setting such as the not-a-knot spline. A brief analysis of the conditioning of the end conditions of cubic spline interpolation leads to a modification of the not-a-knot spline, and some numerical examples suggest that the interpolation error of this revised not-a-knot spline generally is comparable to the near optimal Q-spline and lower than for the not-a-knot spline when the mesh size is small.

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Publication Details

Journal
Journal of Computational and Applied Mathematics
Published
2025-12-04
DOI
https://doi.org/10.1016/j.cam.2025.117240
Primary Topic
Advanced Numerical Analysis Techniques
Type
article
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article

Cubic spline functions revisited

Florian Jarre
Journal of Computational and Applied Mathematics
Advanced Numerical Analysis Techniques
article

Cubic spline functions revisited

Florian Jarre
article en

Abstract

In this paper a fourth order asymptotically optimal error bound for a new cubic interpolating spline function, denoted by Q-spline, is derived for the case that only function values at given points are used but not any derivative information. The bound seems to be stronger than earlier error bounds for cubic spline interpolation in such setting such as the not-a-knot spline. A brief analysis of the conditioning of the end conditions of cubic spline interpolation leads to a modification of the not-a-knot spline, and some numerical examples suggest that the interpolation error of this revised not-a-knot spline generally is comparable to the near optimal Q-spline and lower than for the not-a-knot spline when the mesh size is small.

Journal of Computational and Applied MathematicsVol. 478
Heinrich Heine University Düsseldorf (DE)
Openalex Percentile: Top 61%
Advanced Numerical Analysis Techniques
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