Iterative Methods and Preconditioning for Large and Sparse Linear Systems with Applications

Iterative Methods and Preconditioning for Large and Sparse Linear Systems with Applications

Author: Daniele Bertaccini

Publisher: CRC Press

Published: 2018-02-19

Total Pages: 321

ISBN-13: 1351649612

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This book describes, in a basic way, the most useful and effective iterative solvers and appropriate preconditioning techniques for some of the most important classes of large and sparse linear systems. The solution of large and sparse linear systems is the most time-consuming part for most of the scientific computing simulations. Indeed, mathematical models become more and more accurate by including a greater volume of data, but this requires the solution of larger and harder algebraic systems. In recent years, research has focused on the efficient solution of large sparse and/or structured systems generated by the discretization of numerical models by using iterative solvers.


Iterative Methods and Preconditioners for Systems of Linear Equations

Iterative Methods and Preconditioners for Systems of Linear Equations

Author: Gabriele Ciaramella

Publisher: SIAM

Published: 2022-02-08

Total Pages: 285

ISBN-13: 1611976901

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Iterative methods use successive approximations to obtain more accurate solutions. This book gives an introduction to iterative methods and preconditioning for solving discretized elliptic partial differential equations and optimal control problems governed by the Laplace equation, for which the use of matrix-free procedures is crucial. All methods are explained and analyzed starting from the historical ideas of the inventors, which are often quoted from their seminal works. Iterative Methods and Preconditioners for Systems of Linear Equations grew out of a set of lecture notes that were improved and enriched over time, resulting in a clear focus for the teaching methodology, which derives complete convergence estimates for all methods, illustrates and provides MATLAB codes for all methods, and studies and tests all preconditioners first as stationary iterative solvers. This textbook is appropriate for undergraduate and graduate students who want an overview or deeper understanding of iterative methods. Its focus on both analysis and numerical experiments allows the material to be taught with very little preparation, since all the arguments are self-contained, and makes it appropriate for self-study as well. It can be used in courses on iterative methods, Krylov methods and preconditioners, and numerical optimal control. Scientists and engineers interested in new topics and applications will also find the text useful.


Iterative Methods for Linear Systems

Iterative Methods for Linear Systems

Author: Maxim A. Olshanskii

Publisher: SIAM

Published: 2014-07-21

Total Pages: 257

ISBN-13: 1611973465

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Iterative Methods for Linear Systems?offers a mathematically rigorous introduction to fundamental iterative methods for systems of linear algebraic equations. The book distinguishes itself from other texts on the topic by providing a straightforward yet comprehensive analysis of the Krylov subspace methods, approaching the development and analysis of algorithms from various algorithmic and mathematical perspectives, and going beyond the standard description of iterative methods by connecting them in a natural way to the idea of preconditioning.??


A Survey of Preconditioned Iterative Methods

A Survey of Preconditioned Iterative Methods

Author: Are Magnus Bruaset

Publisher: Routledge

Published: 2018-12-13

Total Pages: 180

ISBN-13: 1351469363

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The problem of solving large, sparse, linear systems of algebraic equations is vital in scientific computing, even for applications originating from quite different fields. A Survey of Preconditioned Iterative Methods presents an up to date overview of iterative methods for numerical solution of such systems. Typically, the methods considered are w


Matrix Preconditioning Techniques and Applications

Matrix Preconditioning Techniques and Applications

Author: Ke Chen

Publisher: Cambridge University Press

Published: 2005-07-14

Total Pages: 616

ISBN-13: 9780521838283

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A comprehensive introduction to preconditioning techniques, now an essential part of successful and efficient iterative solutions of matrices.


Templates for the Solution of Linear Systems

Templates for the Solution of Linear Systems

Author: Richard Barrett

Publisher: SIAM

Published: 1994-01-01

Total Pages: 141

ISBN-13: 9781611971538

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In this book, which focuses on the use of iterative methods for solving large sparse systems of linear equations, templates are introduced to meet the needs of both the traditional user and the high-performance specialist. Templates, a description of a general algorithm rather than the executable object or source code more commonly found in a conventional software library, offer whatever degree of customization the user may desire. Templates offer three distinct advantages: they are general and reusable; they are not language specific; and they exploit the expertise of both the numerical analyst, who creates a template reflecting in-depth knowledge of a specific numerical technique, and the computational scientist, who then provides "value-added" capability to the general template description, customizing it for specific needs. For each template that is presented, the authors provide: a mathematical description of the flow of algorithm; discussion of convergence and stopping criteria to use in the iteration; suggestions for applying a method to special matrix types; advice for tuning the template; tips on parallel implementations; and hints as to when and why a method is useful.


Numerical Methods for Large Eigenvalue Problems

Numerical Methods for Large Eigenvalue Problems

Author: Yousef Saad

Publisher: SIAM

Published: 2011-01-01

Total Pages: 292

ISBN-13: 9781611970739

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This revised edition discusses numerical methods for computing eigenvalues and eigenvectors of large sparse matrices. It provides an in-depth view of the numerical methods that are applicable for solving matrix eigenvalue problems that arise in various engineering and scientific applications. Each chapter was updated by shortening or deleting outdated topics, adding topics of more recent interest, and adapting the Notes and References section. Significant changes have been made to Chapters 6 through 8, which describe algorithms and their implementations and now include topics such as the implicit restart techniques, the Jacobi-Davidson method, and automatic multilevel substructuring.