Graph Theory and Sparse Matrix Computation: 56 (The IMA Volumes in Mathematics and its Applications) - Softcover

9781461383710: Graph Theory and Sparse Matrix Computation: 56 (The IMA Volumes in Mathematics and its Applications)

Softcover

ISBN 10: 1461383714 ISBN 13: 9781461383710

Verlag: Springer, 2011

This book looks at graph theory as it connects to linear algebra, parallel computing, data structures, geometry, and both numerical and discrete 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 analysis and theoretical computer scientists alike.

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Rese�a del editor

This IMA Volume in Mathematics and its Appllcations GRAPH THEORY AND SPARSE MATRIX COMPUTATION is based on the proceedings of a workshop that was an integraI part of the 1991- 92 IMA program on "Applied Linear AIgebra." The purpose of the workshop was to bring together people who work in sparse matrix computation with those who conduct research in applied graph theory and grl:l,ph algorithms, in order to foster active cross-fertilization. We are grateful to Richard Brualdi, George Cybenko, Alan Geo~ge, Gene Golub, Mitchell Luskin, and Paul Van Dooren for planning and implementing the year-Iong program. We espeeially thank Alan George, John R. Gilbert, and Joseph W.H. Liu for organizing this workshop and editing the proceedings. The finaneial support of the National Science Foundation made the workshop possible. A vner Friedman Willard Miller. Jr. PREFACE When reality is modeled by computation, linear algebra is often the con nec� tiori between the continuous physical world and the finite algorithmic one. Usually, the more detailed the model, the bigger the matrix, the better the answer. Efficiency demands that every possible advantage be exploited: sparse structure, advanced com� puter architectures, efficient algorithms. Therefore sparse matrix computation knits together threads from linear algebra, parallei computing, data struetures, geometry, and both numerieal and discrete algorithms.

Rese�a del editor

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.

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VerlagSpringer
Erscheinungsdatum2011
ISBN 10 1461383714
ISBN 13 9781461383710
EinbandTapa blanda
SpracheEnglisch
Anzahl der Seiten264
HerausgeberGeorge Alan, Gilbert John R., Liu Joseph W.H.
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Weitere beliebte Ausgaben desselben Titels

9780387941318: Graph Theory and Sparse Matrix Computation: Workshop on Sparse Matrix Computations: Graph Theory Issues and Algorithms : Selected Papers: 56 (The IMA Volumes in Mathematics and its Applications)

Vorgestellte Ausgabe

ISBN 10: 0387941312 ISBN 13: 9780387941318
Verlag: Springer-Verlag New York Inc., 1993
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Graph Theory and Sparse Matrix Computation

Alan George

Verlag: Springer New York, Springer US, 2011

ISBN 10: 1461383714 ISBN 13: 9781461383710

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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This IMA Volume in Mathematics and its Appllcations GRAPH THEORY AND SPARSE MATRIX COMPUTATION is based on the proceedings of a workshop that was an integraI part of the 1991- 92 IMA program on 'Applied Linear AIgebra.' The purpose of the workshop was to bring together people who work in sparse matrix computation with those who conduct research in applied graph theory and grl:l,ph algorithms, in order to foster active cross-fertilization. We are grateful to Richard Brualdi, George Cybenko, Alan Geo~ge, Gene Golub, Mitchell Luskin, and Paul Van Dooren for planning and implementing the year-Iong program. We espeeially thank Alan George, John R. Gilbert, and Joseph W.H. Liu for organizing this workshop and editing the proceedings. The finaneial support of the National Science Foundation made the workshop possible. A vner Friedman Willard Miller. Jr. PREFACE When reality is modeled by computation, linear algebra is often the con nec tiori between the continuous physical world and the finite algorithmic one. Usually, the more detailed the model, the bigger the matrix, the better the answer. Efficiency demands that every possible advantage be exploited: sparse structure, advanced com puter architectures, efficient algorithms. Therefore sparse matrix computation knits together threads from linear algebra, parallei computing, data struetures, geometry, and both numerieal and discrete algorithms. Artikel-Nr. 9781461383710

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