Numerical Recipes, The Art of Scientific Computing.

Shampine, L. F.; Press, W. H.; Flannery, Brian P.; Teukolsky, Saul A.; Vetterling, William T.

doi:10.2307/2322833

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“…The advice offered does not always correspond to the methods advocated by leading practitioners and implemented in leading libraries. 7 The critique of Numerical Recipes we are most concerned with is the license the programming code is released under. One of two licenses may apply, depending on whether access was purchased as an individual or as an institution.…”

Section: Introductionmentioning

confidence: 99%

Fast Cubic Spline Interpolation

Hornbeck

2020

Preprint

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The Numerical Recipes series of books are a useful resource, but all the algorithms they contain cannot be used within open-source projects. In this paper we develop drop-in alternatives to the two algorithms they present for cubic spline interpolation, showing as much of our work as possible to allow for replication or criticsm. The output of the new algorithms is compared to the old, and found to be no different within the limits imposed by floating-point precision. Benchmarks of all these algorithms, plus variations which may run faster in certain instances, are performed. In general, all these algorithms have approximately the same execution time when interpolating curves with few control points on feature-rich Intel processors; as the number of control points increases or processor features are removed, the new algorithms become consistently faster than the old. Exceptions to that generalization are explored to create implementation guidelines, such as when to expect division to be faster than multiplication.14.50 +2.25 −2.25 12.50 +6.81 −2.50 12.00 +3.73

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Section: Introductionmentioning

confidence: 99%

Fast Cubic Spline Interpolation

Hornbeck

2020

Preprint

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Numerical Recipes, The Art of Scientific Computing.

Cited by 1 publication

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Fast Cubic Spline Interpolation

Fast Cubic Spline Interpolation

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