首页 | 本学科首页   官方微博 | 高级检索  
     


An efficient method for computing eigenvalues of a real normal matrix
Affiliation:1. School of Computing & Mathematics, Deakin University, Geelong, VIC 3217, Australia;2. Oxford University Computing Laboratory, Wolfson Building, Parks Road, Oxford OX1 3QD, UK;1. Department of Management Science, University of Strathclyde, 199 Cathedral Street, G4 0QU Glasgow, United Kingdom;2. Munitions Directorate, Air Force Research Laboratory, Building 13, Eglin AFB, FL32542, United States;1. Mechanical Engineering Department, The University of Alabama, 35487, United States;2. Department of Mechanical and Aerospace Engineering, Florida Institute of Technology, 32901, United States;3. Department of Mechanical Engineering, Michigan State University, United States;1. Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD;2. The Dartmouth Institute for Health Policy and Clinical Practice, Lebanon, NH;3. Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD;4. Johns Hopkins Bloomberg School of Public Health, Baltimore, MD;5. Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD
Abstract:Jacobi-based algorithms have attracted attention as they have a high degree of potential parallelism and may be more accurate than QR-based algorithms. In this paper we discuss how to design efficient Jacobi-like algorithms for eigenvalue decomposition of a real normal matrix. We introduce a block Jacobi-like method. This method uses only real arithmetic and orthogonal similarity transformations and achieves ultimate quadratic convergence. A theoretical analysis is conducted and some experimental results are presented.
Keywords:
本文献已被 ScienceDirect 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号