Behavior in $ L^\infty $ of convolution transforms with dilated kernels
Author(s) -
W. R. Madych
Publication year - 2022
Publication title -
mathematical foundations of computing
Language(s) - English
Resource type - Journals
ISSN - 2577-8838
DOI - 10.3934/mfc.2022005
Subject(s) - limit (mathematics) , bounded function , convolution (computer science) , mathematics , constant (computer programming) , combinatorics , value (mathematics) , mathematical analysis , pure mathematics , physics , mathematical physics , computer science , statistics , machine learning , artificial neural network , programming language
Assuming that \begin{document}$ K(x) $\end{document} is in \begin{document}$ L^1( {\mathbb R}) $\end{document} , \begin{document}$ K_t(x) = t^{-1} K(x/t) $\end{document} , and \begin{document}$ f(x) $\end{document} is in \begin{document}$ L^\infty( {\mathbb R}) $\end{document} , we study the behavior of the convolution \begin{document}$ K_t*f(x) $\end{document} as the parameter \begin{document}$ t $\end{document} tends to \begin{document}$ \infty $\end{document} . It turns out that the limit need not exist and, if it does exist, the limit is a constant independent of \begin{document}$ x $\end{document} . Situations where the limit exists and those where it fails to exist are identified. Several issues related to this are addressed, including the multivariate case. As one application, these results provide an accessible description of the behavior of bounded solutions to the initial value problem for the heat equation.
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