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Graph Theory and Sparse Matrix Computation
     

Graph Theory and Sparse Matrix Computation

by Alan George (Editor), John R. Gilbert (Editor), Joseph W.H. Liu (Editor)
 

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When reality is modeled 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, the better the answer, however, efficiency demands that every possible advantage be exploited. The articles in this volume are based on recent research on sparse

Overview

When reality is modeled 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, the better the answer, however, efficiency demands that every possible advantage be exploited. The articles in this volume are based on recent research on sparse matrix computations. This volume looks at 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. This book will be a resource for the researcher or advanced student of either graphs or sparse matrices; it will be useful to mathematicians, numerical analysts and theoretical computer scientists alike.

Product Details

ISBN-13:
9781461383710
Publisher:
Springer New York
Publication date:
07/31/2012
Series:
IMA Volumes in Mathematics and its Applications Series , #56
Edition description:
Softcover reprint of the original 1st ed. 1993
Pages:
245
Product dimensions:
6.10(w) x 9.25(h) x 0.02(d)

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