Abstract. A detailed new upgrade of the FEAST eigensolver targeting non-Hermitian eigenvalue problems is presented and thoroughly discussed. It aims at broadening the class of eigenproblems that can be addressed within the framework of the FEAST algorithm. The algorithm is ideally suited for computing selected interior eigenvalues and their associated right/left bi-orthogonal eigenvectors, located within a subset of the complex plane. It combines subspace iteration with efficient contour integration techniques that approximate the left and right spectral projectors. We discuss the various algorithmic choices that have been made to improve the stability and usability of the new nonHermitian eigensolver. The latter retains the convergence property and multi-level parallelism of Hermitian FEAST, making it a valuable new software tool for the scientific community.Key words. non-Hermitian eigenproblem, FEAST, spectral projectors, contour integration, right/left eigenvectors, bi-orthogonal vectors AMS subject classifications. 65F15, 15A18 34L16 65Y05 35P991. Introduction. The generalized eigenvalue problem AX = BXΛ with A and B square matrices and Λ diagonal, is a central topic in numerical linear algebra and arises from a broad and diverse set of disciplines in mathematics, science and engineering (the problem is said "standard" if B ≡ I or "generalized" otherwise). Solving the interior eigenvalue problem consists of determining nontrivial solutions {λ i , x i } (i.e. eigenpairs with x i = Xe i and λ i = Λ i,i ) located anywhere inside the spectrum. Most common numerical applications lead to symmetric eigenvalue problems where A is real symmetric or complex Hermitian, B is symmetric or Hermitian positive definite (hpd), and all the obtained eigenvalues λ i are real. Non-symmetric and non-Hermitian eigenvalue problem (including the case where A is complex symmetric) can also be encountered in a variety of situations resulting in complex values for λ i . In this case x i is called the right eigenvector associated with λ i , while one can also define a left eigenvector. Although many software packages are available for symmetric (or Hermitian) matrices (see e.g. [17,23,33,18,25,5,36,20]), relatively few algorithms and software can handle the non-Hermitian problem [22,4,23,3,13]. The FEAST eigensolver [29, 10], proven to be a robust and efficient tool for computing the partial eigenspectrum of Hermitian system matrices [38], can also be generalized and applied to arbitrary non-Hermitian systems [21,41,37].FEAST is a subspace iteration method that uses the Rayleigh-Ritz projection and an approximate spectral projector as a filter [38]. Given a Hermitian generalized eigenvalue problem AX = BXΛ of size n, the algorithm in Figure 1 outlines the main steps of a generic Rayleigh-Ritz subspace iteration procedure for computing m eigenpairs. At convergence, the algorithm yields the B-orthonormal eigensubspace Y m ≡ X m = {x 1 , x 2 , . . . , x m } n×m and associated eigenvalues Λ Q m ≡ Λ m . Taking ρ(B −1 A) = B −1 A, yields the...
Summary This paper discusses techniques for computing a few selected eigenvalue–eigenvector pairs of large and sparse symmetric matrices. A recently developed class of techniques to solve this type of problems is based on integrating the matrix resolvent operator along a complex contour that encloses the interval containing the eigenvalues of interest. This paper considers such contour integration techniques from a domain decomposition viewpoint and proposes two schemes. The first scheme can be seen as an extension of domain decomposition linear system solvers in the framework of contour integration methods for eigenvalue problems, such as FEAST. The second scheme focuses on integrating the resolvent operator primarily along the interface region defined by adjacent subdomains. A parallel implementation of the proposed schemes is described, and results on distributed computing environments are reported. These results show that domain decomposition approaches can lead to reduced run times and improved scalability.
No abstract
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.