Multiparameter eigenvalue problems

Multiparameter eigenvalue problems
Author: Atkinson
Publisher: Academic Press
Total Pages: 226
Release: 1972-06-16
Genre: Computers
ISBN: 0080955908

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Multiparameter eigenvalue problems

Multiparameter Eigenvalue Problems

Multiparameter Eigenvalue Problems
Author: Frederick V. Atkinson
Publisher:
Total Pages: 209
Release: 1972
Genre: Differential equations
ISBN:

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Multiparameter Eigenvalue Problems and Expansion Theorems

Multiparameter Eigenvalue Problems and Expansion Theorems
Author: Hans Volkmer
Publisher: Lecture Notes in Mathematics
Total Pages: 174
Release: 1988-12-07
Genre: Mathematics
ISBN:

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This book provides a self-contained treatment of two of the main problems of multiparameter spectral theory: the existence of eigenvalues and the expansion in series of eigenfunctions. The results are first obtained in abstract Hilbert spaces and then applied to integral operators and differential operators. Special attention is paid to various definiteness conditions which can be imposed on multiparameter eigenvalue problems. The reader is not assumed to be familiar with multiparameter spectral theory but should have some knowledge of functional analysis, in particular of Brower's degree of maps.

Multiparameter Eigenvalue Problems

Multiparameter Eigenvalue Problems
Author: F.V. Atkinson
Publisher: CRC Press
Total Pages: 297
Release: 2010-12-07
Genre: Mathematics
ISBN: 1439816239

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One of the masters in the differential equations community, the late F.V. Atkinson contributed seminal research to multiparameter spectral theory and Sturm-Liouville theory. His ideas and techniques have long inspired researchers and continue to stimulate discussion. With the help of co-author Angelo B. Mingarelli, Multiparameter Eigenvalue Problem

Multiparameter Eigenvalue Problems and Expansion Theorems

Multiparameter Eigenvalue Problems and Expansion Theorems
Author: Hans Volkmer
Publisher: Springer
Total Pages: 164
Release: 2006-11-14
Genre: Mathematics
ISBN: 3540460152

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This book provides a self-contained treatment of two of the main problems of multiparameter spectral theory: the existence of eigenvalues and the expansion in series of eigenfunctions. The results are first obtained in abstract Hilbert spaces and then applied to integral operators and differential operators. Special attention is paid to various definiteness conditions which can be imposed on multiparameter eigenvalue problems. The reader is not assumed to be familiar with multiparameter spectral theory but should have some knowledge of functional analysis, in particular of Brower's degree of maps.

Multiparameter Eigenvalue Problems

Multiparameter Eigenvalue Problems
Author: F. V. Atkinson
Publisher:
Total Pages:
Release: 1972
Genre: Differential equations
ISBN:

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Numerical Methods for Singular Multiparameter Eigenvalue Problems

Numerical Methods for Singular Multiparameter Eigenvalue Problems
Author: Andrej Muhič
Publisher:
Total Pages: 136
Release: 2011
Genre:
ISBN:

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In the 1960s Atkinson introduced an abstract algebraic setting for multiparameter eigenvalue problems. He showed that a nonsingular multiparameter eigenvalue problem is equivalent to the associated system of generalized eigenvalue problems. Many theoretical results and numerical methods for nonsingular multiparameter eigenvalue problems are based on this relation. We extend the above relation to singular two-parameter eigenvalue problems and show that the simple finite regular eigenvalues of a two-parameter eigenvalue problem and the common regular eigenvalues of the coupled generalized eigenvalue problem agree. Using the theory on the pencils of matrix polynomials we furthermore generalize the theory to the nonregular singular two-parameter eigenvalue problems. This enables one to solve a singular two-parameter eigenvalue problem by computing the common regular eigenvalues of the associated system of two singular generalized eigenvalue problems. There are various numerical methods for twoparameter eigenvalue problems, but all of them can only be applied to nonsingular problems. We develop a numerical method that can be applied to the singular two-parameter eigenvalue problems. It is based on the staircase algorithm for the extraction of the common regular part of two singular matrix pencils. We introduce the quadratic two-parameter eigenvalue problem (QMEP) and show that we can linearize it as a regular singular two-parameter eigenvalue problem. We present several transformations that can be used to solve the QMEP, by formulating an associated linear multiparameter eigenvalue problem. We also generalize the linearization to the polynomial twoparameter eigenvalue problem(PMEP). As an alternative approach to the linearization, we propose the transformation of the QMEP into a nonsingular five-parameter eigenvalue problem. We also consider several special cases of the QMEP, where some matrix coefficients are zero, which allows us to solve such QMEP more efficiently. We propose a Jacobi-Davidson type method for regular singular problems. We modify the Jacobi-Davidson type method for nonsingular two-parameter eigenvalue problem so that it can be applied to regular singular problems. The obtained algorithm can then be used to solve the problem obtained by linearizing the PMEP. If the dimension of matrices is large, then we cannot use the approach with linearization. If order of polynomials is small enough, then we can apply a Jacobi-Davidson type method directly to the polynomial system. This method is a generalization of the method for polynomial eigenvalue problems. We give some numerical results that illustrate the convergence of the introduced Jacobi-Davidson type methods.

Numerical Methods for General and Structured Eigenvalue Problems

Numerical Methods for General and Structured Eigenvalue Problems
Author: Daniel Kressner
Publisher: Springer Science & Business Media
Total Pages: 272
Release: 2006-01-20
Genre: Mathematics
ISBN: 3540285024

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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.