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2020 | Buch

Krylov Methods for Nonsymmetric Linear Systems

From Theory to Computations

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Über dieses Buch

This book aims to give an encyclopedic overview of the state-of-the-art of Krylov subspace iterative methods for solving nonsymmetric systems of algebraic linear equations and to study their mathematical properties. Solving systems of algebraic linear equations is among the most frequent problems in scientific computing; it is used in many disciplines such as physics, engineering, chemistry, biology, and several others. Krylov methods have progressively emerged as the iterative methods with the highest efficiency while being very robust for solving large linear systems; they may be expected to remain so, independent of progress in modern computer-related fields such as parallel and high performance computing. The mathematical properties of the methods are described and analyzed along with their behavior in finite precision arithmetic. A number of numerical examples demonstrate the properties and the behavior of the described methods. Also considered are the methods’ implementations and coding as Matlab®-like functions. Methods which became popular recently are considered in the general framework of Q-OR (quasi-orthogonal )/Q-MR (quasi-minimum) residual methods.
This book can be useful for both practitioners and for readers who are more interested in theory. Together with a review of the state-of-the-art, it presents a number of recent theoretical results of the authors, some of them unpublished, as well as a few original algorithms. Some of the derived formulas might be useful for the design of possible new methods or for future analysis. For the more applied user, the book gives an up-to-date overview of the majority of the available Krylov methods for nonsymmetric linear systems, including well-known convergence properties and, as we said above, template codes that can serve as the base for more individualized and elaborate implementations.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction
Abstract
This chapter describes the contents of the book.
Gérard Meurant, Jurjen Duintjer Tebbens
Chapter 2. Notation, definitions and tools
Abstract
In this chapter we set up the notation to be used, we recall some basic notions from linear algebra that are useful for our purposes and we introduce Krylov subspaces and some of their properties.
Gérard Meurant, Jurjen Duintjer Tebbens
Chapter 3. Q-OR and Q-MR methods
Abstract
In the previous chapter we briefly introduced Krylov subspaces. Krylov methods are based on reduction of the problem to small Krylov subspaces whose dimension grows with the iteration number. Most popular Krylov methods can be classified as either a quasi-orthogonal residual (Q-OR) method or a quasi-minimal residual (Q-MR) method, with most Q-OR methods having Q-MR analogs; see [296].
Gérard Meurant, Jurjen Duintjer Tebbens
Chapter 4. Bases for Krylov subspaces
Abstract
In this chapter we consider the computation of bases for the Krylov subspace \(\mathcal{K}_k(A,r_0)\). We could have used another notation for the vector on which the Krylov subspace is constructed, but in the next chapters, the Krylov subspaces we have to deal with will be based on the initial residual vector \(r_0=b-Ax_0\).
Gérard Meurant, Jurjen Duintjer Tebbens
Chapter 5. FOM/GMRES and variants
Abstract
FOM (Full Orthogonalization Method) and GMRES (Generalized Minimal RESidual) is a pair of Q-OR/Q-MR methods using an orthonormal basis for the Krylov subspaces. In fact, as we have seen in Chapter 3, the FOM residual vectors are proportional to the basis vectors. Thus, FOM is a Q-OR method for which the residual vectors are orthogonal to each other. It is a true OR (Orthogonal Residual) method. In GMRES, since the basis is orthonormal, the norm of the quasi-residual  is equal to the norm of the residual. Therefore GMRES is a true MR (Minimum Residual) method. The minimization of the norm of the residual implies that in GMRES the residual norms are decreasing which is a most wanted property for iterative methods. However, as we will see, the GMRES norms may stagnate.
Gérard Meurant, Jurjen Duintjer Tebbens
Chapter 6. Methods equivalent to FOM or GMRES
Abstract
In this chapter we review iterative methods which are equivalent to FOM or GMRES in the sense that, mathematically, they must deliver the same residual norms. However, as we will see, this is not always the case in finite precision arithmetic. The algorithms mathematically equivalent to GMRES either construct residual vectors \(r_k\) orthogonal to \(A\mathcal{K}_k(A,r_0)\) or explicitly minimize the residual norms.
Gérard Meurant, Jurjen Duintjer Tebbens
Chapter 7. Hessenberg/CMRH
Abstract
In this chapter we study the CMRH Krylov method which uses a basis obtained from an LU factorization with partial pivoting of the Krylov matrices. We consider its relations with GMRES.
Gérard Meurant, Jurjen Duintjer Tebbens
Chapter 8. BiCG/QMR and Lanczos algorithms
Abstract
In this chapter we describe Krylov methods using short recurrences, like Lanczos algorithms, BiCG, and QMR which use biorthogonal bases of the Krylov subspaces.
Gérard Meurant, Jurjen Duintjer Tebbens
Chapter 9. Transpose-free Lanczos methods
Abstract
The Lanczos methods studied in Chapter 8 have two shortcomings. First, they can suffer from breakdowns. Second, in each iteration we need to do a matrix-vector product with \(A^T\) (or \(A^*\)). In this chapter we study iterative methods, derived from those of Chapter 8, that do not need a multiplication with the transpose of A. They are sometimes called product-type or transpose-free methods. We consider, particularly, CGS and BiCGStab and their variants.
Gérard Meurant, Jurjen Duintjer Tebbens
Chapter 10. The IDR family
Abstract
This chapter is devoted to Induced Dimension Reduction (IDR) methods. This name comes from the fact that the successive residual vectors are constructed to live in subspaces of decreasing dimensions. We describe several variants and study theirrelations to Krylov subspaces.
Gérard Meurant, Jurjen Duintjer Tebbens
Chapter 11. Restart, deflation and truncation
Abstract
In this chapter we describe techniques that have been used to control the storage and the complexity in methods like GMRES and FOM which use long recurrences to compute the basis vectors. These techniques are restarting and truncation. For restarting, we describe methods like GMRES-DR which is using approximate eigenvectors for computing the restarting vectors.
Gérard Meurant, Jurjen Duintjer Tebbens
Chapter 12. Related topics
Abstract
In this chapter we give pointers to the literature concerning topics that we have not covered in the previous chapters.
Gérard Meurant, Jurjen Duintjer Tebbens
Chapter 13. Numerical comparisons of methods
Abstract
In this chapter we compare numerically some of the methods we have studied in the previous chapters. We chose the methods which seem the most interesting ones and the most widely used.
Gérard Meurant, Jurjen Duintjer Tebbens
Backmatter
Metadaten
Titel
Krylov Methods for Nonsymmetric Linear Systems
verfasst von
Gérard Meurant
Jurjen Duintjer Tebbens
Copyright-Jahr
2020
Electronic ISBN
978-3-030-55251-0
Print ISBN
978-3-030-55250-3
DOI
https://doi.org/10.1007/978-3-030-55251-0