Overview
- Includes supplementary material: sn.pub/extras
Part of the book series: Lecture Notes in Computational Science and Engineering (LNCSE, volume 61)
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About this book
Domain decomposition methods are divide and conquer computational methods for the parallel solution of partial differential equations of elliptic or parabolic type. The methodology includes iterative algorithms, and techniques for non-matching grid discretizations and heterogeneous approximations. This book serves as a matrix oriented introduction to domain decomposition methodology. The topics discussed include hybrid formulations, Schwarz, substructuring and Lagrange multiplier methods for elliptic equations, computational issues, least squares-control methods, multilevel methods, non-self adjoint problems, parabolic equations, saddle point applications (Stokes, porous media and optimal control), non-matching grid discretizations, heterogeneous models, fictitious domain methods, variational inequalities, maximum norm theory, eigenvalue problems, optimization problems and the Helmholtz scattering problem. Selected convergence theory is also included.
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Table of contents (18 chapters)
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Front Matter
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Back Matter
Bibliographic Information
Book Title: Domain Decomposition Methods for the Numerical Solution of Partial Differential Equations
Authors: Tarek Poonithara Abraham Mathew
Series Title: Lecture Notes in Computational Science and Engineering
DOI: https://doi.org/10.1007/978-3-540-77209-5
Publisher: Springer Berlin, Heidelberg
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2008
Softcover ISBN: 978-3-540-77205-7Published: 07 May 2008
eBook ISBN: 978-3-540-77209-5Published: 25 June 2008
Series ISSN: 1439-7358
Series E-ISSN: 2197-7100
Edition Number: 1
Number of Pages: XIV, 770
Number of Illustrations: 40 b/w illustrations
Topics: Analysis, Computational Science and Engineering, Mathematics of Computing, Computational Intelligence, Computational Mathematics and Numerical Analysis, Partial Differential Equations