Publisher DOI: 10.1137/16M1101167
Title: Hierarchical Matrix Approximation for Kernel-Based Scattered Data Interpolation
Language: English
Authors: Iske, Armin 
Le Borne, Sabine  
Wende, Michael 
Issue Date: 3-Oct-2017
Source: SIAM Journal on Scientific Computing 39 (5): A2287-A2316 (2017)
Abstract (english): 
Scattered data interpolation by radial kernel functions leads to linear equation systems with large, fully populated, ill-conditioned interpolation matrices. A successful iterative solution of such a system requires an efficient matrix-vector multiplication as well as an efficient preconditioner. While multipole approaches provide a fast matrix-vector multiplication, they avoid the explicit setup of the system matrix which hinders the construction of preconditioners, such as approximate inverses or factorizations which typically require the explicit system matrix for their construction. In this paper, we propose an approach that allows both an efficient matrix-vector multiplication as well as an explicit matrix representation which can then be used to construct a preconditioner. In particular, the interpolation matrix will be represented in hierarchical matrix format, and several approaches for the blockwise low-rank approximation are proposed and compared, of both analytical nature (separable expansions) and algebraic nature (adaptive cross approximation). The validity of using an approximate system matrix in the iterative solution of the interpolation equations is demonstrated through a range of numerical experiments.
URI: http://hdl.handle.net/11420/4954
ISSN: 0196-5204
Institute: Mathematik E-10 
Document Type: Article
Journal: SIAM journal on scientific computing 
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