Compressing H2 matrices for translationally invariant kernels

R. J. Adams, J. C. Young, S. D. Gedney

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


H2 matrices provide compressed representations of the matrices obtained when discretizing surface and volume integral equations. The memory costs associated with storing H2 matrices for static and low-frequency applications are O(N). However, when the H2 representation is constructed using sparse samples of the underlying matrix, the translation matrices in the H2 representation do not preserve any translational invariance present in the underlying kernel. In some cases, this can result in an H2 representation with relatively large memory requirements. This paper outlines a method to compress an existing H2 matrix by constructing a translationally invariant H2 matrix from it. Numerical examples demonstrate that the resulting representation can provide significant memory savings.

Original languageEnglish
Pages (from-to)1392-1393
Number of pages2
JournalApplied Computational Electromagnetics Society Journal
Issue number11
StatePublished - Nov 2020

Bibliographical note

Funding Information:
This work was supported in part by Office of Naval Research Grant N00014-16-1-3066 and NASA Grant NNX16AO88G.

Publisher Copyright:


  • Integral equations
  • Sparse matrices

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Electrical and Electronic Engineering


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