Large Scale Eigenvalue Problems
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Arpack Users Guide
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Author : Richard B. Lehoucq
language : en
Publisher: SIAM
Release Date : 1998-01-01
Arpack Users Guide written by Richard B. Lehoucq and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-01-01 with Mathematics categories.
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.
Large Scale Eigenvalue Problems
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Author : J. Cullum
language : en
Publisher: Elsevier
Release Date : 1986-01-01
Large Scale Eigenvalue Problems written by J. Cullum and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 1986-01-01 with Mathematics categories.
Results of research into large scale eigenvalue problems are presented in this volume. The papers fall into four principal categories:novel algorithms for solving large eigenvalue problems, novel computer architectures, computationally-relevant theoretical analyses, and problems where large scale eigenelement computations have provided new insight.
Numerical Methods For Large Eigenvalue Problems
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Author : Yousef Saad
language : en
Publisher: SIAM
Release Date : 2011-05-26
Numerical Methods For Large Eigenvalue Problems written by Yousef Saad and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-05-26 with Mathematics categories.
This revised edition discusses numerical methods for computing the eigenvalues and eigenvectors of large sparse matrices. It provides an in-depth view of the numerical methods that are applicable for solving matrix eigenvalue problems that arise in various engineering and scientific applications. Each chapter was updated by shortening or deleting outdated topics, adding topics of more recent interest and adapting the Notes and References section. Significant changes have been made to Chapters 6 through 8, which describe algorithms and their implementations and now include topics such as the implicit restart techniques, the Jacobi-Davidson method and automatic multilevel substructuring.
Templates For The Solution Of Algebraic Eigenvalue Problems
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Author : Zhaojun Bai
language : en
Publisher: SIAM
Release Date : 2000-01-01
Templates For The Solution Of Algebraic Eigenvalue Problems written by Zhaojun Bai and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-01-01 with Computers categories.
Large-scale problems of engineering and scientific computing often require solutions of eigenvalue and related problems. This book gives a unified overview of theory, algorithms, and practical software for eigenvalue problems. It organizes this large body of material to make it accessible for the first time to the many nonexpert users who need to choose the best state-of-the-art algorithms and software for their problems. Using an informal decision tree, just enough theory is introduced to identify the relevant mathematical structure that determines the best algorithm for each problem.
Eigenvalue Problems In Power Systems
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Author : Federico (University College Dublin Milano, Ireland)
language : en
Publisher:
Release Date : 2023-09-25
Eigenvalue Problems In Power Systems written by Federico (University College Dublin Milano, Ireland) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-25 with categories.
The book provides a comprehensive taxonomy of eigenvalue problems supported by examples to study the stability of operating point of power systems and properly designed robust power system controllers. Large real-world networks are used to discuss the scalability of existing techniques and software libraries to solve the eigenvalue problem.
Numerical Methods For General And Structured Eigenvalue Problems
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Author : Daniel Kressner
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-01-20
Numerical Methods For General And Structured Eigenvalue Problems written by Daniel Kressner and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-01-20 with Mathematics categories.
This book is about computing eigenvalues, eigenvectors, and invariant subspaces of matrices. Treatment includes generalized and structured eigenvalue problems and all vital aspects of eigenvalue computations. A unique feature is the detailed treatment of structured eigenvalue problems, providing insight on accuracy and efficiency gains to be expected from algorithms that take the structure of a matrix into account.
Implicity Restarted Arnoldi Lanczos Methods For Large Scale Eigenvalue Calculations
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Author :
language : en
Publisher:
Release Date : 1996
Implicity Restarted Arnoldi Lanczos Methods For Large Scale Eigenvalue Calculations written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with categories.
Matrix Algorithms
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Author : G. W. Stewart
language : en
Publisher: SIAM
Release Date : 1998-08-01
Matrix Algorithms written by G. W. Stewart and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-08-01 with Mathematics categories.
This volume is the first in a self-contained five-volume series devoted to matrix algorithms. It focuses on the computation of matrix decompositions--that is, the factorization of matrices into products of similar ones. The first two chapters provide the required background from mathematics and computer science needed to work effectively in matrix computations. The remaining chapters are devoted to the LU and QR decompositions--their computation and applications. The singular value decomposition is also treated, although algorithms for its computation will appear in the second volume of the series. The present volume contains 65 algorithms formally presented in pseudocode. Other volumes in the series will treat eigensystems, iterative methods, sparse matrices, and structured problems. The series is aimed at the nonspecialist who needs more than black-box proficiency with matrix computations. To give the series focus, the emphasis is on algorithms, their derivation, and their analysis. The reader is assumed to have a knowledge of elementary analysis and linear algebra and a reasonable amount of programming experience, typically that of the beginning graduate engineer or the undergraduate in an honors program. Strictly speaking, the individual volumes are not textbooks, although they are intended to teach, the guiding principle being that if something is worth explaining, it is worth explaining fully. This has necessarily restricted the scope of the series, but the selection of topics should give the reader a sound basis for further study.
Hierarchical Matrices Algorithms And Analysis
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Author : Wolfgang Hackbusch
language : en
Publisher: Springer
Release Date : 2015-12-21
Hierarchical Matrices Algorithms And Analysis written by Wolfgang Hackbusch and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-12-21 with Mathematics categories.
This self-contained monograph presents matrix algorithms and their analysis. The new technique enables not only the solution of linear systems but also the approximation of matrix functions, e.g., the matrix exponential. Other applications include the solution of matrix equations, e.g., the Lyapunov or Riccati equation. The required mathematical background can be found in the appendix. The numerical treatment of fully populated large-scale matrices is usually rather costly. However, the technique of hierarchical matrices makes it possible to store matrices and to perform matrix operations approximately with almost linear cost and a controllable degree of approximation error. For important classes of matrices, the computational cost increases only logarithmically with the approximation error. The operations provided include the matrix inversion and LU decomposition. Since large-scale linear algebra problems are standard in scientific computing, the subject of hierarchical matrices is of interest to scientists in computational mathematics, physics, chemistry and engineering.
Lanczos Algorithms For Large Symmetric Eigenvalue Computations
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Author : Jane K. Cullum
language : en
Publisher: SIAM
Release Date : 2002-09-01
Lanczos Algorithms For Large Symmetric Eigenvalue Computations written by Jane K. Cullum and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-09-01 with Mathematics categories.
First published in 1985, this book presents background material, descriptions, and supporting theory relating to practical numerical algorithms for the solution of huge eigenvalue problems. This book deals with 'symmetric' problems. However, in this book, 'symmetric' also encompasses numerical procedures for computing singular values and vectors of real rectangular matrices and numerical procedures for computing eigenelements of nondefective complex symmetric matrices. Although preserving orthogonality has been the golden rule in linear algebra, most of the algorithms in this book conform to that rule only locally, resulting in markedly reduced memory requirements. Additionally, most of the algorithms discussed separate the eigenvalue (singular value) computations from the corresponding eigenvector (singular vector) computations. This separation prevents losses in accuracy that can occur in methods which, in order to be able to compute further into the spectrum, use successive implicit deflation by computed eigenvector or singular vector approximations.