{"title":"海报:预条件共轭梯度的基于数值的排序","authors":"J. Booth","doi":"10.1109/SC.Companion.2012.309","DOIUrl":null,"url":null,"abstract":"The ordering of a matrix vastly impact the convergence rate of precondition conjugate gradient method. Past ordering methods focus solely on a graph representation of the sparse matrix and do not give an inside into the convergence rate that is linked to the preconditioned eigenspectrum. This work attempt to investigate how numerical based ordering may produce a better preconditioned system in terms of faster convergence.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"91 1","pages":"1534-1534"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Poster: Numeric Based Ordering for Preconditioned Conjugate Gradient\",\"authors\":\"J. Booth\",\"doi\":\"10.1109/SC.Companion.2012.309\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The ordering of a matrix vastly impact the convergence rate of precondition conjugate gradient method. Past ordering methods focus solely on a graph representation of the sparse matrix and do not give an inside into the convergence rate that is linked to the preconditioned eigenspectrum. This work attempt to investigate how numerical based ordering may produce a better preconditioned system in terms of faster convergence.\",\"PeriodicalId\":6346,\"journal\":{\"name\":\"2012 SC Companion: High Performance Computing, Networking Storage and Analysis\",\"volume\":\"91 1\",\"pages\":\"1534-1534\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-11-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 SC Companion: High Performance Computing, Networking Storage and Analysis\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SC.Companion.2012.309\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SC.Companion.2012.309","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Poster: Numeric Based Ordering for Preconditioned Conjugate Gradient
The ordering of a matrix vastly impact the convergence rate of precondition conjugate gradient method. Past ordering methods focus solely on a graph representation of the sparse matrix and do not give an inside into the convergence rate that is linked to the preconditioned eigenspectrum. This work attempt to investigate how numerical based ordering may produce a better preconditioned system in terms of faster convergence.