Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/44369
Title: Convergence of multilevel stationary Gaussian convolution
Authors: Hubbert, Simon
Levesley, Jeremy
First Published: 5-Jan-2019
Presented at: Numerical Mathematics and Advanced Applications ENUMATH 2017.
Publisher: Springer Verlag (Germany)
Citation: Lecture Notes in Computational Science and Engineering, 2019, 126, pp. 83-92 Numerical Mathematics and Advanced Applications ENUMATH 2017.
Abstract: In this paper we give a short note showing convergence rates for periodic approximation of smooth functions by multilevel Gaussian convolution. We will use the Gaussian scaling in the convolution at the finest level as a proxy for degrees of freedom d in the model. We will show that, for functions in the native space of the Gaussian, convergence is of the order (Formula Presented.). This paper provides a baseline for what should be expected in discrete convolution, which will be the subject of a follow up paper.
DOI Link: 10.1007/978-3-319-96415-7_5
ISSN: 1439-7358
Links: https://link.springer.com/chapter/10.1007%2F978-3-319-96415-7_5
http://hdl.handle.net/2381/44369
Embargo on file until: 5-Jan-2020
Version: Post-print
Status: Peer-reviewed
Type: Conference Paper
Rights: Copyright © 2019, Springer Verlag (Germany). Deposited with reference to the publisher’s open access archiving policy. (http://www.rioxx.net/licenses/all-rights-reserved)
Description: The file associated with this record is under embargo until 12 months after publication, in accordance with the publisher's self-archiving policy. The full text may be available through the publisher links provided above.
Appears in Collections:Conference Papers & Presentations, Dept. of Mathematics

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