A parallel generalized conjugate gradient method for large scale eigenvalue problems

被引：1
作者：
Yu Li
Hehu Xie
Ran Xu
Chun’guang You
Ning Zhang
机构：
[1] Tianjin University of Finance and Economics,Coordinated Innovation Center for Computable Modeling in Management Science
[2] ICMSEC,School of Mathematical Sciences
[3] LSEC,undefined
[4] NCMIS,undefined
[5] Academy of Mathematics and Systems Science,undefined
[6] Chinese Academy of Sciences,undefined
[7] University of Chinese Academy of Sciences,undefined
[8] CAEP Software Center for High Performance Numerical Simulation,undefined
[9] Institute of Electrical Engineering,undefined
[10] Chinese Academy of Sciences,undefined
来源：
CCF Transactions on High Performance Computing | 2020年 / 2卷
关键词：
Large scale eigenvalue problem; Dumping blocked inverse power; Parallel generalized conjugate gradient; Efficiency; Stability; scalability; 65N30; 65B99;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Based on damping blocked inverse power method, a type of generalized parallel conjugate gradient method is proposed for large scale eigenvalue problems. Techniques for orthogonalization and computing Rayleigh-Ritz problems are introduced to improve the stability, efficiency and scalability. Furthermore, a computing package is built based on the proposed method here. Some numerical tests are provided to validate the stability, efficiency and scalability of the method in this paper. The corresponding computing package can be downloaded from the web site:  https://github.com/pase2017/GCGE-1.0.
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页码：111 / 122
页数：11
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