solution of large-scale eigenvalue problems with implicitly restarted Arnoldi methods /
First Statement of Responsibility
R.B. Lehoucq, D.C. Sorensen, C. Yang.
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
Philadelphia, Pa. :
Name of Publisher, Distributor, etc.
Society for Industrial and Applied Mathematics,
Date of Publication, Distribution, etc.
1998.
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
1 online resource (xv, 142 p.) :
Other Physical Details
ill.
SERIES
Series Title
Software, environments, tools ;
Volume Designation
6.
INTERNAL BIBLIOGRAPHIES/INDEXES NOTE
Text of Note
Includes bibliographical references (p. 133-136) and index.
CONTENTS NOTE
Text of Note
List of figures -- List of tables -- Preface -- 1. Introduction to ARPACK -- Important features -- Getting started -- Reverse communication interface -- Availability -- Installation -- Documentation -- Dependence on LAPACK and BLAS -- Expected performance -- P_ARPACK -- Contributed additions -- Trouble shooting and problems -- 2. Getting started with ARPACK -- Directory structure and contents -- Getting started -- An example for a symmetric eigenvalue problem -- 3. General use of ARPACK -- Naming conventions, precisions, and types -- Shift and invert spectral transformation mode -- Reverse communication structure for shift-invert -- Using the computational modes -- Computational modes for real symmetric problems -- Postprocessing for eigenvectors using dseupd -- Computational modes for real nonsymmetric problems -- Postprocessing for eigenvectors using dneupd -- Computational modes for complex problems -- Postprocessing for eigenvectors using zneupd -- 4. The implicitly restarted Arnoldi method -- Structure of the eigenvalue problem -- Krylov subspaces and projection methods -- The Arnoldi factorization -- Restarting the Arnoldi method -- The generalized eigenvalue problem -- Stopping criterion -- 5. Computational routines -- ARPACK subroutines -- LAPACK routines used by ARPACK -- BLAS routines used by ARPACK -- Appendix A. Templates and driver routines -- Symmetric Drivers -- Real nonsymmetric drivers -- Complex divers -- Band drivers -- The singular value decomposition -- Appendix B. Tracking the progress of ARPACK -- Obtaining trace output -- Check-pointing ARPACK -- Appendix C. The XYaupd ARPACK routines -- DSAUPD -- DNAUPD -- ZNAUPD -- Bibliography -- Index.
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SUMMARY OR ABSTRACT
Text of Note
This book is a guide to understanding and using the software package ARPACK to solve large algebraic eigenvalue problems. The software described is based on the implicitly restarted Arnoldi method, which has been heralded as one of the three most important advances in large scale eigenanalysis in the past ten years. The book explains the acquisition, installation, capabilities, and detailed use of the software for computing a desired subset of the eigenvalues and eigenvectors of large (sparse) standard or generalized eigenproblems. It also discusses the underlying theory and algorithmic background at a level that is accessible to the general practitioner.