The Origin and Development of Krylov Subspace Methods

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Abstract

Krylov subspace methods have had unparalleled success in solving real-life problems across disciplines ranging from computational fluid dynamics to statistics, machine learning, control theory, and computational chemistry, among many others. This article provides a brief history of these methods, discussing their origin, their expansion, and the lives of the people behind them.

Original languageEnglish (US)
Pages (from-to)28-39
Number of pages12
JournalComputing in Science and Engineering
Volume24
Issue number4
DOIs
StatePublished - Jul 1 2022

Bibliographical note

Funding Information:
I would like to thank Ron Boisvert for invitingme to take part in the symposium titled 75th Anniversary of Mathematics and Statistics at NIST, and Barry Schneider for the idea that I contribute this article to CiSE. The manuscript benefitted from numerous helpful suggestions made by Lorena Barba. I wish to also thank Ahmed Sameh for his comments and encouragements on an earlier draft. A historical article like this one cannot be written without available resources provided by others. The authors of these articles and testimonals should be commended and thanked for graciously taking the time and effort to contribute to our knowledge of the science and the lives of the people behind it. In this regard, the most fascinating and informative read for me in preparing this article has been the book on Lanczos's life by Barbara Gellai.12 I also read with considerable interest the article by Hestenes and Todd,18an impressive document that discussed in great detail the history and impact of the Institute of Numerical Analysis at NBS. This work was supported by NSF under Grant DMS 1912048.

Publisher Copyright:
© 1999-2011 IEEE.

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