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Iterative Methods For Sparse Linear Systems Version 2 Book Freeshipping

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iterative methods sparse linear systems version 2 book freeshipping
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Iterative Methods for Sparse Linear Systems (Version 2), by Yousef Saad, English, Hardcover, 2009



Author Yousef Saad

Press Science Press

Isbn 9787030234834

Publication Date Jan 2009

Pages 528

Size 16K

Edition Second

Cover Hardcover

Language English

Page material gelatine plate paper

Product Description:

This book can be used to teach graduate-level courses on iterative methods for linear systems. Engineers and mathematicians will find its contents easily accessible, and practitioners and educators will value it as a helpful resource. The preface includes syllabi that can be used for either a semester- or quarter-length course in both mathematics and computer science.Iterative Methods for Sparse Linear Systems, Second Edition 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.

This new edition includes a wide range of the best methods available today. The author has added a new chapter on multigrid techniques and has updated material throughout the text, particularly the chapters on sparse matrices, Krylov subspace methods, preconditioning techniques, and parallel preconditioners. Material on older topics has been removed or shortened, numerous exercises have been added, and many typographical errors have been corrected. The updated and expanded bibliography now includes more recent works emphasizing new and important research topics in this field.


Preface to the Second Edition

Preface to the First Edition

1 Background in Linear Algebra

1.1 Matrices

1.2 Square Matrices and Eigenvalues

1.3 Types of Matrices

1.4 Vector Inner Products and Norms

1.5 Matrix Norms

1.6 Subspaces, Range, and Kernel

1.7 Orthogonal Vectors and Subspaces

1.8 Canonical Forms of Matrices

1.8.1 Reduction to the Diagonal Form

1.8.2 The Jordan Canonical Form

1.8.3 The Schur Canonical Form

1.8.4 Application to Powers of Matrices

1.9 Normal and Hermitian Matrices

1.9.1 Normal Matrices

1.9.2 Hermitian Matrices

1.10 Nonnegative Matrices, M-Matrices

1.11 Positive Definite Matrices

1.12 Projection Operators

1.12.1 Range and Null Space of a Projector

1.12.2 Matrix Representations

1.12.3 Orthogonal and Oblique Projectors

1.12.4 Properties of Orthogonal Projectors

1.13 Basic Concepts in Linear Systems

1.13.1 Existence of a Solution

1.13.2 Perturbation Analysis

2 Discretization of Partial Differential Equations

2.1 Partial Differential Equations

2.1.1 Elliptic Operators

2.1.2 The Convection Diffusion Equation

2.2 Finite Difference Methods

2.2.1 Basic Approximations

2.2.2 Difference Schemes for the Laplacian Operator

2.2.3 Finite Differences for One-Dimensional Problerr

2.2.4 Upwind Schemes

2.2.5 Finite Differences for Two-Dimensional Problerr

2.2.6 Fast Poisson Solvers

2.3 The Finite Element Method

2.4 Mesh Generation and Refinement

2.5 Finite Volume Method

3 Sparse Matrices

3.1 Introduction

3.2 Graph Representations

3.2.1 Graphs and Adjacency Graphs

3.2.2 Graphs of PDE Matrices

3.3 Permutations and Reorderings

3.3.1 Basic Concepts

3.3.2 Relations with the Adjacency Graph

3.3.3 Common Reorderings

3.3.4 Irreducibility

3.4 Storage Schemes

3.5 Basic Sparse Matrix Operations

3.6 Sparse Direct Solution Methods

3.6.1 MD Ordering

3.6.2 ND Ordering

3.7 Test Problems

4 Basic Iterative Methods

4.1 Jacobi, Gauss-Seidel, and Successive Overrelaxation

4.1.1 Block Relaxation Schemes

4.1.2 Iteration Matrices and Preconditioning

4.2 Convergence

4.2.1 General Convergence Result

4.2.2 Regular Splittings

4.2.3 Diagonally Dominant Matrices

4.2.4 Symmetric Positive Definite Matrices

4.2.5 Property A and Consistent Orderings

4.3 Alternating Direction Methods

5 Projection Methods

5.1 Basic Definitions and Algorithms

5.1.1 General Projection Methods

5.1.2 Matrix Representation

5.2 General Theory

5.2.1 Two Optimality Results

5.2.2 Interpretation in Terms of Projectors

5.2.3 General Error Bound

5.3 One-Dimensional Projection Processes

5.3.1 Steepest Descent

5.3.2MR Iteration

5.3.3 Residual Norm Steepest Descent

5.4 Additive and Multiplicative Processes

6 Kryiov Subspace Methods, Part 1

6.1 Introduction

6.2 Krylov Subspaces

6.3 Arnoldi's Method

6.3.1 The Basic Algorithm

6.3.2 Practical Implementations

6.4 Arnoldi's Method for Linear Systems

6.4.1 Variation 1: Restarted FOM

6.4.2Variation 2: IOM and DIOM

6.5 Generalized Minimal Residual Method

6.5.1 The Basic GMRES Algorithm

6.5.2 The Householder Version

6.5.3 Practical Implementation Issues

6.5.4 Breakdown of GMRES

6.5.5 Variation 1: Restarting

6.5.6 Variation 2: Truncated GMRES Versions

6.5.7 Relations Between FOM and GMRES

6.5.8 Residual Smoothing

6.5.9 GMRES for Complex Systems

6.6 The Symmetric Lanczos Algorithm

6.6.1 The Algorithm

6.6.2 Relation to Orthogonal Polynomials

6.7 The Conjugate Gradient Algorithm

6.7.1 Derivation and Theory "

6.7.2 Alternative Formulations

6.7.3 Eigenvalue Estimates from the CG Coefficients

6.8 The Conjugate Residual Method

6.9 Generalized Conjugate Residual, ORTHOMIN, and ORTHODIR

6.10 The Faber-Manteuffel Theorem

6.11 Convergence Analysis

6.11.1 Real Chebyshev Polynomials

6.11.2 Complex Chebyshev Polynomials

6.11.3 Convergence of the CG Algorithm

6.11.4 Convergence of GMRES

6.12 Block Krylov Methods

7 Kryiov Subspaee Methods, Part 2

7.1 Lanczos Biorthogonalization

7.1.1 The Algorithm

7.1.2 Practical Implementations

7.2 The Lanczos Algorithm for Linear Systems

7.3 The Biconjugate Gradient and Quasi-Minimal Residual Algorithms

7.3.1 The BCG Algorithm

7.3.2 QMR Algorithm

7.4 Transpose-Free Variants

7.4.1 cgs

7.4.2 bicgstab

7.4.3 tfqmr

8 Methods Related to the Normal Equations

8.1 The Normal Equations

8.2 Row Projection Methods

8.2.1 Gauss-Seidel on the Normal Equations

8.2.2 Cimmino's Method

8.3 Conjugate Gradient and Normal Equations

8.3.1 cgnr

8.3.2 cgne

8.4 Saddle-Point Problems

9 Preconditioned Iterations

9.1 Introduction

9.2 Preconditioned Conjugate Gradient

9.2.1 Preserving Symmetry

9.2.2 Efficient Implementations

10 Preconditioning Techniques

11 Parallel Implementations

12 Parallel Preconditioners

13 Multigrid Methods

14 Domain Decomposition Methods



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