<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">WEI ZHAO</style></author><author><style face="normal" font="default" size="100%">MARTIN STOLL</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">STABILITY ON INTERPOLATION OF SCATTERED DATA VIA KERNELS</style></title><secondary-title><style face="normal" font="default" size="100%">Neural, Parallel, and Scientific Computations</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">39A10</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2017</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://acadsol.eu/npsc/articles/25/1/5.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">25</style></volume><pages><style face="normal" font="default" size="100%">15</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;For an approximation, the inverse inequality can guarantee the smoothness of an approximant based on its rate approximation. The purpose of this paper is to present new inverse inequalities for scattered data interpolation on&amp;nbsp;${ \mathbb{R}^d }$&amp;nbsp;and bounded domain ${ Ω.}$&amp;nbsp; Finally, some numerical experiments are given as well.&lt;/p&gt;

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</style></abstract><section><style face="normal" font="default" size="100%">45</style></section></record></records></xml>