The solutions of systems of linear and nonlinear equations occurs in many situations and is therefore a question of major interest. Advances in computer technology has made it now possible to consider systems exceeding several hundred thousands of equations. However, there is a crucial need for more efficient algorithms.

The main focus of this book (except the last chapter, which is devoted to systems of nonlinear equations) is the consideration of solving the problem of the linear equation Ax = b by an iterative method. Iterative methods for the solution of this question are described which are based on projections. Recently, such methods have received much attention from researchers in numerical linear algebra and have been applied to a wide range of problems.

The book is intended for students and researchers in numerical analysis and for practitioners and engineers who require the most recent methods for solving their particular problem.

Table of Contents

Introduction. 1. Preliminaries. Projections and projection methods. Projections. Projection methods. Extrapolation methods. Best approximation. Algorithms for recursive projection. The general interpolation problem. Recursive projection. Solving linear systems by extrapolation. The topological &egr;-algorithm. The S-&bgr;-algorithm. Vector Padé approximants. Vector &thgr;-type transformations. 2. Biorthogonality. Generalities. Biorthogonal polynomials. Hankel and Toeplitz systems. Hankel matrices. Toeplitz matrices. Biorthogonalization processes. 3. Projection Methods for Linear Systems. Variational formulation. Projection iterative methods. Method of conjugate directions. The conjugate gradients algorithm. Row projection methods. Projection acceleration procedures. Preconditioned steepest descent algorithms. Accelerated descent methods. 4. Lanczos-Type Methods. Extrapolation and projection methods. Vorobyev's method of moments. The method of Lanczos. Generalizations of Lanczos' method. Orthores. Lanczos/Orthores. The method of Arnoldi. 5. Hybrid Procedures. The basic procedure. Recursive use of the procedure. Convergence acceleration. Multiple hybrid procedures. Changing the minimilization criterion. 6. Semi-Iterative Methods. Semi-iterative hybrid procedures. More projection methods. Stationary iterative methods. Nonstationary iterative methods. The minimal residual smoothing method. A hybrid minimal residual smoothing method. 7. Around Richardson's Projection. The basic idea. Choice of the search direction. Choice of the preconditioner. Constant preconditioner. Linear iterative preconditioner. Quadratic iterative preconditioner. Direct preconditioner. A sparse preconditioner. Numerical examples. Another choice for the search direction. Two-step methods. Splitting-up methods. Multiparameter extensions. The symmetric positive defini


© 1997
North Holland
Print ISBN:
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About the editor

C. Brezinski

Affiliations and Expertise

University of Lille, Villeneuve d'Ascq, France


@from:G. Walz @qu:In the authors words, 'this book is mainly intended for researchers in the filed'. The reviewer would add: 'but it is also very interesting for students as well as for researchers in other fields'. I have learned a lot from this book, it is well written, it contains nice theoretical results as well as many algorithms and numerical examples. I think that it is a valuable contribution to numerical linear algebra. @source:Zentralblatt für Mathematik @from:A. Bultheel excellent guide to the literature. ...In conlusion, the book is intended for researchers in the field, but can also be read by advanced students, for whom it may open opportunities for new directions of research. @source:Newsletter on Computational Applied Mathematics, Vol.15, No.1 @qu:...useful to anyone interested in the most recent results about this important class of methods. @source:Mathematical Reviews