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Iterative Methods and Preconditioning for Large and Sparse Linear Systems with Applications - Info and Reading Options

"Iterative Methods and Preconditioning for Large and Sparse Linear Systems with Applications" was published by Taylor & Francis Group in 2018 - Milton, it has 354 pages and the language of the book is English.


“Iterative Methods and Preconditioning for Large and Sparse Linear Systems with Applications” Metadata:

  • Title: ➤  Iterative Methods and Preconditioning for Large and Sparse Linear Systems with Applications
  • Authors:
  • Language: English
  • Number of Pages: 354
  • Publisher: Taylor & Francis Group
  • Publish Date:
  • Publish Location: Milton

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Cover -- Half title -- Published Titles -- Title -- Copyright -- Dedication -- Contents -- Preface -- List of Figures -- List of Tables -- List of Algorithms -- Chapter 1 Introduction and Motivations -- 1.1 Notes on error analysis -- 1.2 Sparse matrices -- 1.3 On parallel computing and hardware acceleration -- Chapter 2 Some iterative algorithms for linear systems -- 2.1 Iterative algorithms and simple one-dimensional techniques -- 2.2 Krylov subspace algorithms, orthogonal projections: CG and GMRES -- 2.3 Krylov subspace algorithms, oblique projections: BiCG, QMR, CGS, BiCGstab, BiCGstab(l -- 2.4 Which Krylov subspace method should I use -- Chapter 3 General purpose preconditioning strategies -- 3.1 Generalities on preconditioning -- 3.2 Krylov iterative methods for preconditioned iterations -- 3.3 On Jacobi, SOR, and SSOR preconditioners -- 3.4 Incomplete factorization preconditioners -- 3.5 Approximate inverse preconditioners -- 3.6 Preconditioning sequences of linear systems -- 3.7 Parallelizing preconditioners and available software -- 3.8 Which general purpose preconditioner should I use -- Chapter 4 Preconditioners for some structured linear systems -- 4.1 Toeplitz and block Toeplitz systems -- 4.2 Notes on multigrid preconditioning -- 4.3 Complex symmetric systems -- 4.4 Saddle point systems -- Appendix A A Review of Numerical Linear Algebra -- A.1 Vector and matrix norms -- A.2 Eigenvalues and singular values -- Appendix B Data sets and software codes -- B.1 Test matrices and data sets -- B.2 Numerical Linear Algebra software -- Bibliography -- Index

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