Large linear systems of equations arise in most scientific problems where mathematical models are used. The most efficient methods for solving these equations are iterative methods. The first part of this book contains basic and classical material from the study of linear algebra and numerical linear algebra. The second half of the book is unique among books on this topic, because it is devoted to the construction of preconditioners and iterative acceleration methods of the conjugate gradient type. This book is for graduate students and researchers in numerical analysis and applied mathematics.
The book may be attractive to researchers dealing with large linear systems of equations, and hoping to solve these in some quick fashion. Axelsson discusses iterative methods that he claims can converge rapidly. While this may not be generally true, the pragmatic researcher might keep Axelsson's ideas in mind, and consider applying them to her problems. The first section of the book is somewhat mundane. Totally standard descriptions that can be found in many texts on linear algebra. But the value of the book is in the second section. Where the author offers neat suggestions on how to pick the initial conditions for starting the iterations. As well as a type of steepest gradient descent to speed up the convergence.
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