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Iterative methods for sparse linear systems

By: Saad, YMaterial type: Text

TextPublication details: Philadelphia : SIAM, c2003Edition: 2nd edDescription: xviii, 528 pages : illustrationsISBN: 9780898715347; 0898715342 Subject(s): Sparse matrices | Iterative methods (Mathematics) | Differential equations, PartialDDC classification: 512.9434 Online resources: Click here to access online | Click here to access online

Contents:

Background in linear algebra -- Discretization of partial differential equations -- Sparse matrices -- Basic iterative methods -- Projection methods -- Krylov subspace methods, part I -- Krylov subspace methods, part II -- Methods related to the normal equations -- Preconditioned iterations -- Preconditioning techniques -- Parallel implementations -- Parallel preconditioners -- Multigrid methods -- Domain decomposition methods.

Summary: "[This] gives an in-depth, up-to-date view of practical algorithms for solving large-scale linear systems of equations. These equations can number in the millions and are sparse in the sense that each involves only a small number of unknowns. The methods described are iterative, i.e., they provide sequences of approximations that will converge to the solution"--Back cover.

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Item type	Current library	Collection	Call number	Status	Date due	Barcode	Item holds
Permanent Reference	Main Library Permanent Reference	Reference	512.9434 SAA (Browse shelf(Opens below))	Not for loan		015022

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Includes Index

Background in linear algebra --
Discretization of partial differential equations --
Sparse matrices --
Basic iterative methods --
Projection methods --
Krylov subspace methods, part I --
Krylov subspace methods, part II --
Methods related to the normal equations --
Preconditioned iterations --
Preconditioning techniques --
Parallel implementations --
Parallel preconditioners --
Multigrid methods --
Domain decomposition methods.

"[This] gives an in-depth, up-to-date view of practical algorithms for solving large-scale linear systems of equations. These equations can number in the millions and are sparse in the sense that each involves only a small number of unknowns. The methods described are iterative, i.e., they provide sequences of approximations that will converge to the solution"--Back cover.

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