Robust Algebraic Multilevel Methods and Algorithms

Kraus, Johannes/Svetozar Margenov

ISBN 10: 3110193655 / ISBN 13: 9783110193657

Published by Walter de Gruyter Verlag, 2009

About this Item

This book deals with algorithms for the solution of linear systems of algebraic equations with large-scale sparse matrices, with a focus on problems that are obtained after discretization of partial differential equations using finite element methods. X,246 Seiten, gebunden (Radon Series on Computational and Applied Mathematics; Vol. 5/Walter de Gruyter Verlag 2009). Statt EUR 124,95. Gewicht: 560 g - Gebunden/Gebundene Ausgabe - Sprache: Englisch.

Seller Inventory # 31440

Bibliographic Details

• Title: Robust Algebraic Multilevel Methods and Algorithms
• Publisher: Walter de Gruyter Verlag
• Publication Date: 2009
• Language: English
• ISBN 10: 3110193655
• ISBN 13: 9783110193657
• Binding: Hardcover
• Condition: Neu
• Weight: 560 grams
• Seller catalogs:
  • Mathematik

About this title

Synopsis

This book deals with algorithms for the solution of linear systems of algebraic equations with large-scale sparse matrices, with a focus on problems that are obtained after discretization of partial differential equations using finite element methods. The authors provide a systematic presentation of the recent advances in robust algebraic multilevel methods and algorithms, e.g., the preconditioned conjugate gradient method, algebraic multilevel iteration (AMLI) preconditioners, the classical algebraic multigrid (AMG) method and its recent modifications, namely AMG using element interpolation (AMGe) and AMG based on smoothed aggregation.

The first six chapters can serve as a short introductory course on the theory of AMLI methods and algorithms. The next part of the monograph is devoted to more advanced topics, including the description of new generation AMG methods, AMLI methods for discontinuous Galerkin systems, looking-free algorithms for coupled problems etc., ending with important practical issues of implementation and challenging applications. This second part is addressed to some more experienced students and practitioners and can be used to complete a more advanced course on robust AMLI and AMG methods and their efficient application.

This book is intended for mathematicians, engineers, natural scientists etc.

About the Author

Johannes Kraus, Radon Institute for Computational and Applied Mathematics, Linz, Austria;
Svetozar Margenov, Institute for Parallel Processing, Sofia, Bulgaria.