Multigrid Methods: Axiomatic Convergence Theory for Linear and Weakly Nonlinear Problems - Paperback

by Abner J. Salgado (Author), Steven M. Wise (Author), Calvin Wong (Author)

This book introduces the theory and application of multigrid methods for the fast numerical solution of linear and weakly nonlinear elliptic PDE. No previous exposure to numerical discretization methods is assumed. All that is required of the reader is curiosity and some basic knowledge of matrix theory and the theory of finite-dimensional vector spaces.

We use an axiomatic, mostly-matrix-based approach in the book, both as a way of presenting the theory in a natural and simple setting, and as a means for translating the theory into practical codes. We deviate a little from the matrix-based-approach in the presentation of the framework for nonlinear problems in the latter part of the book. That nonlinear analysis, based on subspace decompositions, represents an area of current research. In fact, the book takes the reader all the way from the basics and simple implementation issues to the front lines of multigrid research.

Coding the multigrid method is notoriously difficult. The current book, which contains several sample codes in the finite element and cell-centered finite difference frameworks, will train the interested reader in the construction of sophisticated, efficient multigrid codes using the simple but powerful MATLAB(c) programming environment.

Author Biography

Abner J. Salgado is a professor of mathematics at the University of Tennessee, Knoxville. His main area of research is the numerical analysis of nonlinear partial differential equations, and related questions. He has authored more that 60 publications and is the co-author of the graduate-level textbook "Classical Numerical Analysis," published by Cambridge University Press.

Steven Wise is a professor of mathematics at the University of Tennessee, Knoxville. He conducts research in the areas of partial differential equations, numerical analysis, and scientific computing, with applications in problems from thermodynamics, fluid mechanics, biophysics, and materials science. He is the author of over 120 journal articles and two books, including a graduate-level numerical analysis textbook with Abner Salgado.

Calvin (Ming Hei) Wong is a Ph.D. candidate in mathematics at the University of Tennessee, Knoxville. His ongoing research is focusing on Diffuse Domain Methods and machine-learning approaches in computational applied math.