Title
Refined error estimates for Green kernel-based interpolation
Abstract
Positive-definite kernels are probably best known for their application in many problems driven by scattered data interpolation. Fasshauer and Ye introduced constructive theory of reproducing kernels of generalized Sobolev spaces in 2011 to provide insight into the types of functions being well approximated by these kernels on a set of scattered points. In this approach, the reproducing kernel is viewed as the Green kernel of a suitable differential operator with some boundary conditions. Sampling inequalities and the minimum norm property in reproducing kernel Hilbert spaces (RKHSs) bring out the standard error bound; however, this estimate is valid only when the target functions belong to the native spaces of the Green kernels. In this paper we provide Sobolev-type error estimates for cases in which the target functions are smoother than functions in the native space. The results are useful and effective for the error analysis of Green kernel-based interpolation problems. (C) 2022 Elsevier Ltd. All rights reserved.
Year
DOI
Venue
2022
10.1016/j.aml.2022.108258
APPLIED MATHEMATICS LETTERS
Keywords
DocType
Volume
Positive-definite kernels, Green kernels, Error estimates
Journal
133
ISSN
Citations 
PageRank 
0893-9659
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Hamed Mohebalizadeh100.34
Gregory E. Fasshauer220.72
Hojatollah Adibi300.34