{"title":"线性系统结构草图","authors":"Johannes J Brust, Michael A Saunders","doi":"arxiv-2407.00746","DOIUrl":null,"url":null,"abstract":"For linear systems $Ax=b$ we develop iterative algorithms based on a\nsketch-and-project approach. By using judicious choices for the sketch, such as\nthe history of residuals, we develop weighting strategies that enable short\nrecursive formulas. The proposed algorithms have a low memory footprint and\niteration complexity compared to regular sketch-and-project methods. In a set\nof numerical experiments the new methods compare well to GMRES, SYMMLQ and\nstate-of-the-art randomized solvers.","PeriodicalId":501215,"journal":{"name":"arXiv - STAT - Computation","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Structured Sketching for Linear Systems\",\"authors\":\"Johannes J Brust, Michael A Saunders\",\"doi\":\"arxiv-2407.00746\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For linear systems $Ax=b$ we develop iterative algorithms based on a\\nsketch-and-project approach. By using judicious choices for the sketch, such as\\nthe history of residuals, we develop weighting strategies that enable short\\nrecursive formulas. The proposed algorithms have a low memory footprint and\\niteration complexity compared to regular sketch-and-project methods. In a set\\nof numerical experiments the new methods compare well to GMRES, SYMMLQ and\\nstate-of-the-art randomized solvers.\",\"PeriodicalId\":501215,\"journal\":{\"name\":\"arXiv - STAT - Computation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-06-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - STAT - Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2407.00746\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - STAT - Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2407.00746","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
For linear systems $Ax=b$ we develop iterative algorithms based on a
sketch-and-project approach. By using judicious choices for the sketch, such as
the history of residuals, we develop weighting strategies that enable short
recursive formulas. The proposed algorithms have a low memory footprint and
iteration complexity compared to regular sketch-and-project methods. In a set
of numerical experiments the new methods compare well to GMRES, SYMMLQ and
state-of-the-art randomized solvers.