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Krylov methods for nonsymmetric line...

Duintjer Tebbens, Jurjen.

Krylov methods for nonsymmetric linear systemsfrom theory to computations /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Krylov methods for nonsymmetric linear systemsby Gerard Meurant, Jurjen Duintjer Tebbens.
Reminder of title:	from theory to computations /
Author:	Meurant, Gerard.
other author:	Duintjer Tebbens, Jurjen.
Published:	Cham :Springer International Publishing :2020.
Description:	xiv, 686 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Algebras, Linear.
Online resource:	https://doi.org/10.1007/978-3-030-55251-0
ISBN:	9783030552510$q(electronic bk.)

Krylov methods for nonsymmetric linear systemsfrom theory to computations /
Meurant, Gerard.

Krylov methods for nonsymmetric linear systemsfrom theory to computations /[electronic resource] :by Gerard Meurant, Jurjen Duintjer Tebbens. - Cham :Springer International Publishing :2020. - xiv, 686 p. :ill., digital ;24 cm. - Springer series in computational mathematics,v.570179-3632 ;. - Springer series in computational mathematics ;42..

1. Notation, definitions and tools -- 2. Q-OR and Q-MR methods -- 3. Bases for Krylov subspaces -- 4. FOM/GMRES and variants -- 5. Methods equivalent to FOM or GMRES- 6. Hessenberg/CMRH -- 7. BiCG/QMR and Lanczos algorithms -- 8. Transpose-free Lanczos methods -- 9. The IDR family -- 10. Restart, deflation and truncation -- 11. Related topics -- 12. Numerical comparison of methods -- A. Test matrices and short biographical notices -- References -- Index.

This book aims to give an encyclopedic overview of the state-of-the-art of Krylov subspace iterative methods for solving nonsymmetric systems of algebraic linear equations and to study their mathematical properties. Solving systems of algebraic linear equations is among the most frequent problems in scientific computing; it is used in many disciplines such as physics, engineering, chemistry, biology, and several others. Krylov methods have progressively emerged as the iterative methods with the highest efficiency while being very robust for solving large linear systems; they may be expected to remain so, independent of progress in modern computer-related fields such as parallel and high performance computing.The mathematical properties of the methods are described and analyzed along with their behavior in finite precision arithmetic. A number of numerical examples demonstrate the properties and the behavior of the described methods. Also considered are the methods' implementations and coding as Matlab®-like functions. Methods which became popular recently are considered in the general framework of Q-OR (quasi-orthogonal )/Q-MR (quasi-minimum) residual methods. This book can be useful for both practitioners and for readers who are more interested in theory. Together with a review of the state-of-the-art, it presents a number of recent theoretical results of the authors, some of them unpublished, as well as a few original algorithms. Some of the derived formulas might be useful for the design of possible new methods or for future analysis. For the more applied user, the book gives an up-to-date overview of the majority of the available Krylov methods for nonsymmetric linear systems, including well-known convergence properties and, as we said above, template codes that can serve as the base for more individualized and elaborate implementations.

ISBN: 9783030552510$q(electronic bk.)

Standard No.: 10.1007/978-3-030-55251-0doiSubjects--Topical Terms:

183087
Algebras, Linear.

LC Class. No.: QA184.2

Dewey Class. No.: 512.5

Krylov methods for nonsymmetric linear systemsfrom theory to computations /
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245 1 0 $a Krylov methods for nonsymmetric linear systems $h [electronic resource] : $b from theory to computations / $c by Gerard Meurant, Jurjen Duintjer Tebbens.
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505 0 $a 1. Notation, definitions and tools -- 2. Q-OR and Q-MR methods -- 3. Bases for Krylov subspaces -- 4. FOM/GMRES and variants -- 5. Methods equivalent to FOM or GMRES- 6. Hessenberg/CMRH -- 7. BiCG/QMR and Lanczos algorithms -- 8. Transpose-free Lanczos methods -- 9. The IDR family -- 10. Restart, deflation and truncation -- 11. Related topics -- 12. Numerical comparison of methods -- A. Test matrices and short biographical notices -- References -- Index.
520 $a This book aims to give an encyclopedic overview of the state-of-the-art of Krylov subspace iterative methods for solving nonsymmetric systems of algebraic linear equations and to study their mathematical properties. Solving systems of algebraic linear equations is among the most frequent problems in scientific computing; it is used in many disciplines such as physics, engineering, chemistry, biology, and several others. Krylov methods have progressively emerged as the iterative methods with the highest efficiency while being very robust for solving large linear systems; they may be expected to remain so, independent of progress in modern computer-related fields such as parallel and high performance computing.The mathematical properties of the methods are described and analyzed along with their behavior in finite precision arithmetic. A number of numerical examples demonstrate the properties and the behavior of the described methods. Also considered are the methods' implementations and coding as Matlab®-like functions. Methods which became popular recently are considered in the general framework of Q-OR (quasi-orthogonal )/Q-MR (quasi-minimum) residual methods. This book can be useful for both practitioners and for readers who are more interested in theory. Together with a review of the state-of-the-art, it presents a number of recent theoretical results of the authors, some of them unpublished, as well as a few original algorithms. Some of the derived formulas might be useful for the design of possible new methods or for future analysis. For the more applied user, the book gives an up-to-date overview of the majority of the available Krylov methods for nonsymmetric linear systems, including well-known convergence properties and, as we said above, template codes that can serve as the base for more individualized and elaborate implementations.
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