Abstract
$H^{2}$ matrices provide compressed representations of the matrices obtained when discretizing surface and volume integral equations. The memory costs associated with storing $H^{2}$ matrices for static and low-frequency applications are $O(N)$ . However, when the $H^{2}$ representation is constructed using sparse samples of the underlying matrix, the translation matrices in the $H^{2}$ representation do not preserve any translational invariance present in the underlying kernel. In some cases, this can result in an $H^{2}$ representation with relatively large memory requirements. This paper outlines a method to compress an existing $H^{2}$ matrix by constructing a translationally invariant $H^{2}$ matrix from it. Numerical examples demonstrate that the resulting representation can provide significant memory savings.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
