Compressing H2 Matrices for Translationally Invariant Kernels

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 sa...

Full description

Saved in:
Bibliographic Details
Published in:Applied Computational Electromagnetics Society journal 2020-01, Vol.35 (11), p.1392
Main Authors: Adams, R J, Young, J C, Gedney, S D
Format: Article
Language:English
Subjects:
Computer engineering
Integral equations
Invariants
Representations
Volume integral equations
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page
container_issue 11
container_start_page 1392
container_title Applied Computational Electromagnetics Society journal
container_volume 35
creator Adams, R J
Young, J C
Gedney, S D
description 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.
format article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2908966160</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2908966160</sourcerecordid><originalsourceid>FETCH-proquest_journals_29089661603</originalsourceid><addsrcrecordid>eNqNi70KwjAYAIMoWH_eIeBcSJo2TQenolTErXsJkkpKTOr3pYJvbwcfwOluuFuQhFe5SIuS8-XsrMjTXKlyTTaIA2NCiVIm5FiH5wgG0foHbTJ60xHs3SDtA9AWtEenow1eO_ehF__WYLWP9GrAG4c7suq1Q7P_cUsO51NbN-kI4TUZjN0QJphn7LKKqUpKLpn4r_oCfQI5cw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2908966160</pqid></control><display><type>article</type><title>Compressing H2 Matrices for Translationally Invariant Kernels</title><source>IEEE Xplore All Conference Series</source><source>Publicly Available Content Database</source><source>EZB Free E-Journals</source><creator>Adams, R J ; Young, J C ; Gedney, S D</creator><creatorcontrib>Adams, R J ; Young, J C ; Gedney, S D</creatorcontrib><description>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.</description><identifier>ISSN: 1054-4887</identifier><identifier>EISSN: 1943-5711</identifier><language>eng</language><publisher>Pisa: River Publishers</publisher><subject>Computer engineering ; Integral equations ; Invariants ; Representations ; Volume integral equations</subject><ispartof>Applied Computational Electromagnetics Society journal, 2020-01, Vol.35 (11), p.1392</ispartof><rights>2020. This work is published under https://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2908966160?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,25753,37012,44590</link.rule.ids></links><search><creatorcontrib>Adams, R J</creatorcontrib><creatorcontrib>Young, J C</creatorcontrib><creatorcontrib>Gedney, S D</creatorcontrib><title>Compressing H2 Matrices for Translationally Invariant Kernels</title><title>Applied Computational Electromagnetics Society journal</title><description>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.</description><subject>Computer engineering</subject><subject>Integral equations</subject><subject>Invariants</subject><subject>Representations</subject><subject>Volume integral equations</subject><issn>1054-4887</issn><issn>1943-5711</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNqNi70KwjAYAIMoWH_eIeBcSJo2TQenolTErXsJkkpKTOr3pYJvbwcfwOluuFuQhFe5SIuS8-XsrMjTXKlyTTaIA2NCiVIm5FiH5wgG0foHbTJ60xHs3SDtA9AWtEenow1eO_ehF__WYLWP9GrAG4c7suq1Q7P_cUsO51NbN-kI4TUZjN0QJphn7LKKqUpKLpn4r_oCfQI5cw</recordid><startdate>20200101</startdate><enddate>20200101</enddate><creator>Adams, R J</creator><creator>Young, J C</creator><creator>Gedney, S D</creator><general>River Publishers</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20200101</creationdate><title>Compressing H2 Matrices for Translationally Invariant Kernels</title><author>Adams, R J ; Young, J C ; Gedney, S D</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_29089661603</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Computer engineering</topic><topic>Integral equations</topic><topic>Invariants</topic><topic>Representations</topic><topic>Volume integral equations</topic><toplevel>online_resources</toplevel><creatorcontrib>Adams, R J</creatorcontrib><creatorcontrib>Young, J C</creatorcontrib><creatorcontrib>Gedney, S D</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies &amp; Aerospace Database‎ (1962 - current)</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>Applied Computational Electromagnetics Society journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Adams, R J</au><au>Young, J C</au><au>Gedney, S D</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Compressing H2 Matrices for Translationally Invariant Kernels</atitle><jtitle>Applied Computational Electromagnetics Society journal</jtitle><date>2020-01-01</date><risdate>2020</risdate><volume>35</volume><issue>11</issue><spage>1392</spage><pages>1392-</pages><issn>1054-4887</issn><eissn>1943-5711</eissn><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.</abstract><cop>Pisa</cop><pub>River Publishers</pub><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1054-4887
ispartof Applied Computational Electromagnetics Society journal, 2020-01, Vol.35 (11), p.1392
issn 1054-4887
1943-5711
language eng
recordid cdi_proquest_journals_2908966160
source IEEE Xplore All Conference Series; Publicly Available Content Database; EZB Free E-Journals
subjects Computer engineering
Integral equations
Invariants
Representations
Volume integral equations
title Compressing H2 Matrices for Translationally Invariant Kernels
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-23T09%3A24%3A15IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Compressing%20H2%20Matrices%20for%20Translationally%20Invariant%20Kernels&rft.jtitle=Applied%20Computational%20Electromagnetics%20Society%20journal&rft.au=Adams,%20R%20J&rft.date=2020-01-01&rft.volume=35&rft.issue=11&rft.spage=1392&rft.pages=1392-&rft.issn=1054-4887&rft.eissn=1943-5711&rft_id=info:doi/&rft_dat=%3Cproquest%3E2908966160%3C/proquest%3E%3Cgrp_id%3Ecdi_FETCH-proquest_journals_29089661603%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2908966160&rft_id=info:pmid/&rfr_iscdi=true