This second edition preserves the introductory spirit of the first edition while roughly doubling the amount of material covered. The topics of the first edition have been enhanced with additional discussion, new numerical experiments, and improved figures. New topics in the second edition include nonlinear equations, Neumann boundary conditions, variable mesh and variable coefficient problems, anisotropic problems, algebraic multigrid (AMG), adaptive methods, and finite elements. This introductory book is ideally suited as a companion textbook for graduate numerical analysis courses. It is written for computational mathematicians, engineers, and other scientists interested in learning about multigrid.
This book is open access under a CC BY license. This book offers a concise and gentle introduction to finite element programming in Python based on the popular FEniCS software library.
This comprehensive textbook is designed for first-year graduate students from a variety of engineering and scientific disciplines.
Mathematics of Computing -- General.
Since the dawn of computing, the quest for a better understanding of Nature has been a driving force for technological development.
The book presents analytical as well as algorithmic aspects. This text can serve as an indispensable reference for researchers, graduate students, and practitioners.
This book constitutes the thoroughly refereed post-conference proceedings of the Second International Conference on High Performance Computing and Applications, HPCA 2009, held in Shangahi, China, in August 2009.
V. KUMAR, A. GRAMA, A. GUPTA, AND G. KARYPIs, Introduction to Parallel Computing: Design and Analysis ofAlgorithms, Benjamin/Cummings Publishing Company, Redwood City, CA, 1994. C.-H. LAI, P. E. BJoRsTAD, M. CRoss, AND O. B. WIDLUND, ...
Readers with a practical interest in multi-grid methods will benefit from this book as well as readers with a more theoretical interest.
Scientific Python is taught from scratch in this book via copious, downloadable, useful and adaptable code snippets.
This classic text presents the best practices of developing multigrid solvers for large-scale computational problems in science and engineering.