Least Squares and the Not-Normal Equations

IF 6.1 1区 数学 Q1 MATHEMATICS, APPLIED
SIAM Review Pub Date : 2025-11-06 DOI:10.1137/23m161851x
Andrew J. Wathen
{"title":"Least Squares and the Not-Normal Equations","authors":"Andrew J. Wathen","doi":"10.1137/23m161851x","DOIUrl":null,"url":null,"abstract":"SIAM Review, Volume 67, Issue 4, Page 865-872, December 2025. <br/> Abstract.For many of the classic problems of linear algebra, effective and efficient numerical algorithms exist, particularly for situations where dimensions are not too large. The linear least squares problem is one such example: excellent algorithms exist when [math] factorization is feasible. However, for large-dimensional (often sparse) linear least squares problems there currently exist good solution algorithms only for well-conditioned problems or for problems where there are lots of data but only a few variables in the solution. Such approaches ubiquitously employ normal equations and so have to contend with conditioning issues. We explore some alternative approaches that we characterize as not-normal equations where conditioning may not be such an issue.","PeriodicalId":49525,"journal":{"name":"SIAM Review","volume":"42 1","pages":""},"PeriodicalIF":6.1000,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"SIAM Review","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1137/23m161851x","RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, APPLIED","Score":null,"Total":0}
引用次数: 0

Abstract

SIAM Review, Volume 67, Issue 4, Page 865-872, December 2025.
Abstract.For many of the classic problems of linear algebra, effective and efficient numerical algorithms exist, particularly for situations where dimensions are not too large. The linear least squares problem is one such example: excellent algorithms exist when [math] factorization is feasible. However, for large-dimensional (often sparse) linear least squares problems there currently exist good solution algorithms only for well-conditioned problems or for problems where there are lots of data but only a few variables in the solution. Such approaches ubiquitously employ normal equations and so have to contend with conditioning issues. We explore some alternative approaches that we characterize as not-normal equations where conditioning may not be such an issue.
最小二乘法和非正态方程
SIAM评论,第67卷,第4期,第865-872页,2025年12月。摘要。对于线性代数的许多经典问题,存在有效和高效的数值算法,特别是在维数不是太大的情况下。线性最小二乘问题就是这样一个例子:当[数学]分解可行时,就存在优秀的算法。然而,对于大维度(通常是稀疏的)线性最小二乘问题,目前存在的良好的求解算法仅适用于条件良好的问题或具有大量数据但解中只有少数变量的问题。这种方法普遍使用标准方程,因此必须与条件反射问题作斗争。我们探索了一些替代方法,我们将其描述为非正常方程,其中条件作用可能不是这样的问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
SIAM Review
SIAM Review 数学-应用数学
CiteScore
16.90
自引率
0.00%
发文量
50
期刊介绍: Survey and Review feature papers that provide an integrative and current viewpoint on important topics in applied or computational mathematics and scientific computing. These papers aim to offer a comprehensive perspective on the subject matter. Research Spotlights publish concise research papers in applied and computational mathematics that are of interest to a wide range of readers in SIAM Review. The papers in this section present innovative ideas that are clearly explained and motivated. They stand out from regular publications in specific SIAM journals due to their accessibility and potential for widespread and long-lasting influence.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信
小红书