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Polynomial Based Iteration Methods for Symmetric Linear Systems - Softcover

 
9783663111092: Polynomial Based Iteration Methods for Symmetric Linear Systems

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Any book on the solution of nonsingular systems of equations is bound to start with Ax= J, but here, A is assumed to be symmetric. These systems arise frequently in scientific computing, for example, from the discretization by finite differences or by finite elements of partial differential equations. Usually, the resulting coefficient matrix A is large, but sparse. In many cases, the need to store the matrix factors rules out the application of direct solvers, such as Gaussian elimination in which case the only alternative is to use iterative methods. A natural way to exploit the sparsity structure of A is to design iterative schemes that involve the coefficient matrix only in the form of matrix-vector products. To achieve this goal, most iterative methods generate iterates Xn by the simple rule Xn = Xo + Qn-l(A)ro, where ro = f-Axo denotes the initial residual and Qn-l is some polynomial of degree n - 1. The idea behind such polynomial based iteration methods is to choose Qn-l such that the scheme converges as fast as possible.

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Contents: Introduction - Orthogonal Polynomials - Chebyshev and Optimal Polynomials - Orthogonal Polynomials and Krylow Subspaces - Estimating the Spectrum and the Distribution function - Parameter Free Methods - Parameter Dependent Methods - The Stokes Problem - Approximating the A-Norm - Bibliography - Notation - Index

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  • PublisherVieweg+Teubner Verlag
  • Publication date2013
  • ISBN 10 3663111091
  • ISBN 13 9783663111092
  • BindingPaperback
  • LanguageGerman
  • Number of pages283

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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Contents: Introduction - Orthogonal Polynomials - Chebyshev and Optimal Polynomials - Orthogonal Polynomials and Krylow Subspaces - Estimating the Spectrum and the Distribution function - Parameter Free Methods - Parameter Dependent Methods - The Stokes Problem - Approximating the A-Norm - Bibliography - Notation - Index. Seller Inventory # 9783663111092

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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 1 Introduction.- 2 Orthogonal Polynomials.- 3 Chebyshev and Optimal Polynomials.- 4 Orthogonal Polynomials and Krylov Subspaces.- 5 Estimating the Spectrum and the Distribution function.- 6 Parameter Free Methods.- 7 Parameter Dependent Methods.- 8 The Stok. Seller Inventory # 5231046

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Taschenbuch. Condition: Neu. Neuware -Any book on the solution of nonsingular systems of equations is bound to start with Ax= J, but here, A is assumed to be symmetric. These systems arise frequently in scientific computing, for example, from the discretization by finite differences or by finite elements of partial differential equations. Usually, the resulting coefficient matrix A is large, but sparse. In many cases, the need to store the matrix factors rules out the application of direct solvers, such as Gaussian elimination in which case the only alternative is to use iterative methods. A natural way to exploit the sparsity structure of A is to design iterative schemes that involve the coefficient matrix only in the form of matrix-vector products. To achieve this goal, most iterative methods generate iterates Xn by the simple rule Xn = Xo + Qn-l(A)ro, where ro = f-Axo denotes the initial residual and Qn-l is some polynomial of degree n - 1. The idea behind such polynomial based iteration methods is to choose Qn-l such that the scheme converges as fast as possible.Springer Vieweg in Springer Science + Business Media, Abraham-Lincoln-Straße 46, 65189 Wiesbaden 284 pp. Deutsch. Seller Inventory # 9783663111092

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