28 September 2016 Modelling reduced sparse data
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Proceedings Volume 10031, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2016; 100314V (2016) https://doi.org/10.1117/12.2249260
Event: Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2016, 2016, Wilga, Poland
Abstract
In this paper we discuss the problem of fitting to an ordered collection of points in arbitary Euclidean space called reduced data. We are not given here the corresponding interpolation knots. Instead, these are estimated by new knots upon minimizing a relevant highly nonlinear optimization scheme based on natural spline interpolation. The existence of a global minimizer (i.e. the collection of interpolation knots in ascending order) is also addressed in this paper. Finally, Leap-Frog optimization tool is used to compute these knots approximating the unknown interpolation knots. This numerical scheme is subsequently compared with the Secant Method. Two illustrative examples are given.
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Ryszard Kozera, Lyle Noakes, "Modelling reduced sparse data", Proc. SPIE 10031, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2016, 100314V (28 September 2016); doi: 10.1117/12.2249260; https://doi.org/10.1117/12.2249260
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