Direct methods for sparse matrices
著者
書誌事項
Direct methods for sparse matrices
（Monographs on numerical analysis）
Clarendon Press, 1989
- : pbk
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注記
Bibliography: p. [313]-326
Includes indexes
First published in paperback (with corrections) 1989
内容説明・目次
内容説明
The subject of sparse matrices has its roots in such diverse fields as management science, power systems analysis, surveying, circuit theory, and structural analysis. Mathematical models in all these areas give rise to very large systems of linear equations which could not be solved were it not for the fact that the matrices contain relatively few non-zero entries. Only comparatively recently, in the last fifteen years or so, has it become apparent that the equations can be solved even when the pattern is irregular, and it is primarily the solution of such problems that is considered in this book. The subject is intensely practical and this book is written with practicalities ever in mind. Whenever two methods are applicable, their rival merits are considered, and conclusions are based on practical experience where theoretical comparison is not possible. Non-numeric computing techniques have been included, as well as frequent illustrations, in an attempt to bridge the usually wide gap between the printed page and the working computer code.
Despite this practical bias, it is recognized that many aspects of the subject are of interest in their own right, and the book aims to be suitable also for a student course, probably at M.Sc. level. Exercises have been included to illustrate and strengthen understanding of the material, as well as to extend it. Efficient use of sparsity is a key to solving large problems in many fields. This book will supply both insight and answers for those attempting to solve these problems.
目次
- Introduction
- Sparse matrices: storage schemes and simple operations
- Gaussian elimination for dense matrices: the algebraic approach
- Gaussian elimination for dense matrices: numerical considerations
- Gaussian elimination for sparse matrices: an introduction
- Reduction to block triangular form
- Local pivotal strategies for sparse matrices
- Ordering sparse matrices to special forms
- Implementing Gaussian elimination: ANALYSE with numerical values
- Implementing Gaussian elimination with symbolic ANALYSE
- Partitioning, matrix modification, and tearing
- Other sparsity-oriented issues
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