Graph theory and sparse matrix computation
Author(s)
Bibliographic Information
Graph theory and sparse matrix computation
（The IMA volumes in mathematics and its applications, v. 56）
Springer-Verlag, c1993
- : us
- : gw
Available at / 32 libraries
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Science and Technology Library, Kyushu University
: us101/GRA068252193011741,
415.7/G 35068252194000356 -
Library, Research Institute for Mathematical Sciences, Kyoto University数研
C-P||Minneapolis||1991.1093059736
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Note
"Proceedings of a workshop that was an integral part of the 1991-92 IMA program on "Applied Linear Algebra"" -- Foreword
Includes bibliographical references
Description and Table of Contents
- Volume
-
: us ISBN 9780387941318
Table of Contents
An introduction to chordal graphs and clique trees.- Cutting down on fill using nested dissection: Provably good elimination orderings.- Automatic Mesh Partitioning.- Structural representations of Schur complements in sparse matrices.- Irreducibility and primitivity of Perron complements: Application of the compressed directed graph.- Predicting structure in nonsymmetric sparse matrix factorizations.- Highly parallel sparse triangular solution.- The fan-both family of column-based distributed Cholesky factorization algorithms.- Scalability of sparse direct solvers.- Sparse matrix factorization on SIMD parallel computers.- The efficient parallel iterative solution of large sparse linear systems.
- Volume
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: gw ISBN 9783540941316
Description
When reality is modelled by computation, matrices are often the connection between the continuous physical world and the finite algorithmic one. Usually, the more detailed the model, the bigger the matrix; however, efficiency demands that every possible advantage be exploited. The articles in this volume are based on recent research on sparse matrix computations. They examine graph theory as it connects to linear algebra, parallel computing, data structures, geometry and both numerical and discrete algorithms. The articles are grouped into three general categories: graph models of symmetric matrices and factorizations; graph models of algorithms on nonsymmetric matrices; and parallel sparse matrix algorithms.
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