The eigenvalues slicing library (EVSL): Algorithms, implementation, and software

作     者：Li, Ruipeng Xi, Yuanzhe Erlandson, Lucas Saad, Yousef 

作者机构：Center for Applied Scientific Computing Lawrence Livermore National Laboratory P. O. Box 808 L-561 LivermoreCA94551 Department of Computer Science and Engineering University of Minnesota Twin Cities MinneapolisMN55455 United States 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2018年

核心收录：

主　　题：Spectral density 

摘      要：This paper describes a software package called EVSL (for EigenValues Slicing Library) for solving large sparse real symmetric standard and generalized eigenvalue problems. As its name indicates, the package exploits spectrum slicing, a strategy that consists of dividing the spectrum into a number of subintervals and extracting eigenpairs from each subinterval independently. In order to enable such a strategy, the methods implemented in EVSL rely on a quick calculation of the spectral density of a given matrix, or a matrix pair. What distinguishes EVSL from other currently available packages is that EVSL relies entirely on filtering techniques. Polynomial and rational filtering are both implemented and are coupled with Krylov subspace methods and the subspace iteration algorithm. On the implementation side, the package offers interfaces for various scenarios including matrix-free modes, whereby the user can supply his/her own functions to perform matrix-vector operations or to solve sparse linear systems. The paper describes the algorithms in EVSL, provides details on their implementations, and discusses performance issues for the various methods. Copyright © 2018, The Authors. All rights reserved.