Compressing H2Matrices for Translationally Invariant Kernels

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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
Title of host publication2020 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2020
ISBN (Electronic)9781733509602
DOIs
StatePublished - Jul 2020
Event2020 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2020 - Virtual, Monterey, United States
Duration: Jul 27 2020Jul 31 2020

Publication series

Name2020 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2020

Conference

Conference2020 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2020
Country/TerritoryUnited States
CityVirtual, Monterey
Period7/27/207/31/20

Bibliographical note

Publisher Copyright:
© 2020 Applied Computational Electromagnetics Society (ACES).

Keywords

  • integral equations
  • sparse matrices

ASJC Scopus subject areas

  • Radiation
  • Computer Networks and Communications
  • Electrical and Electronic Engineering
  • Parasitology
  • Instrumentation

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