15 research outputs found
Interior Eigensolver Based on Rational Filter with Composite rule
Contour integral based rational filter leads to interior eigensolvers for
non-Hermitian generalized eigenvalue problems. Based on the Zolotarev's
problems, this paper proves the asymptotic optimality of the trapezoidal
quadrature of the contour integral in terms of the rational function
separation. A composite rule of the trapezoidal quadrature is derived. Two
interior eigensolvers are proposed based on the composite rule. Both
eigensolvers adopt direct factorization and multi-shift generalized minimal
residual method for the inner and outer rational functions, respectively. The
first eigensolver fixes the order of the outer rational function and applies
the subspace iteration to achieve convergence, whereas the second eigensolver
doubles the order of the outer rational function every iteration to achieve
convergence without subspace iteration. The efficiency and stability of
proposed eigensolvers are demonstrated on synthetic and practical sparse matrix
pencils.Comment: 28 pages,26 figure
Computing eigenvalues of real symmetric matrices\ud with rational filters in real arithmetic
Powerful algorithms have recently been proposed for computing eigenvalues of large matrices by methods related to contour integrals; best known are the works of Sakurai and coauthors and Polizzi and coauthors. Even if the matrices are real symmetric, most such methods rely on complex arithmetic, leading to expensive linear systems to solve. An appealing technique for overcoming this starts from the observation that certain discretized contour integrals are equivalent to rational interpolation problems, for which there is no need to leave the real axis. Investigation shows that using rational interpolation per se suffers from instability; however, related techniques involving real rational filters can be very effective. This article presents a technique of this kind that is related to previous work published in Japanese by Murakami
Efficient variants of the CMRH method for solving a sequence of multi-shifted non-Hermitian linear systems simultaneously
Multi-shifted linear systems with non-Hermitian coefficient matrices arise in
numerical solutions of time-dependent partial/fractional differential equations
(PDEs/FDEs), in control theory, PageRank problems, and other research fields.
We derive efficient variants of the restarted Changing Minimal Residual method
based on the cost-effective Hessenberg procedure (CMRH) for this problem class.
Then, we introduce a flexible variant of the algorithm that allows to use
variable preconditioning at each iteration to further accelerate the
convergence of shifted CMRH. We analyse the performance of the new class of
methods in the numerical solution of PDEs and FDEs, also against other
multi-shifted Krylov subspace methods.Comment: Techn. Rep., Univ. of Groningen, 34 pages. 11 Tables, 2 Figs. This
manuscript was submitted to a journal at 20 Jun. 2016. Updated version-1: 31
pages, 10 tables, 2 figs. The manuscript was resubmitted to the journal at 9
Jun. 2018. Updated version-2: 29 pages, 10 tables, 2 figs. Make it concise.
Updated version-3: 27 pages, 10 tables, 2 figs. Updated version-4: 28 pages,
10 tables, 2 fig
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Inexact and Nonlinear Extensions of the FEAST Eigenvalue Algorithm
Eigenvalue problems are a basic element of linear algebra that have a wide variety of applications. Common examples include determining the stability of dynamical systems, performing dimensionality reduction on large data sets, and predicting the physical properties of nanoscopic objects. Many applications require solving large dimensional eigenvalue problems, which can be very challenging when the required number of eigenvalues and eigenvectors is also large. The FEAST algorithm is a method of solving eigenvalue problems that allows one to calculate large numbers of eigenvalue/eigenvector pairs by using contour integration in the complex plane to divide the large number of desired pairs into many small groups; these small groups of eigenvalue/eigenvector pairs may then be simultaneously calculated independently of each other. This makes it possible to quickly solve eigenvalue problems that might otherwise be very difficult to solve efficiently.
The standard FEAST algorithm can only be used to solve eigenvalue problems that are linear, and whose matrices are small enough to be factorized efficiently (thus allowing linear systems of equations to be solved exactly). This limits the size and the scope of the problems to which the FEAST algorithm may be applied. This dissertation describes extensions of the standard FEAST algorithm that allow it to efficiently solve nonlinear eigenvalue problems, and eigenvalue problems whose matrices are large enough that linear systems of equations can only be solved inexactly