@article{b7162d339a3247bc80fa6db7ce349b5f,
title = "Deflated block Krylov subspace methods for large scale eigenvalue problems",
abstract = "We discuss a class of deflated block Krylov subspace methods for solving large scale matrix eigenvalue problems. The efficiency of an Arnoldi-type method is examined in computing partial or closely clustered eigenvalues of large matrices. As an improvement, we also propose a refined variant of the Arnoldi-type method. Comparisons show that the refined variant can further improve the Arnoldi-type method and both methods exhibit very regular convergence behavior.",
keywords = "Arnoldi process, Krylov subspace, Refined approximate eigenvector, Ritz value, Ritz vector",
author = "Qiang Niu and Linzhang Lu",
note = "Funding Information: This work is supported by the National Natural Science Foundation of China (No. 10961010). Funding Information: The first author would like to thank Prof. Zhaojun Bai and Prof. Ren-Cang Li for their lectures presented at the LSEC Summer School on Numerical Linear Algebra, and many valuable discussions. In addition, sincere thanks go to Michael Ng and anonymous referees for their valuable comments that considerably improved this work. A part of the first author{\textquoteright}s work was performed during this author{\textquoteright}s visit to INRIA, funded by the China Scholarship Council.",
year = "2010",
month = jun,
day = "1",
doi = "10.1016/j.cam.2009.11.058",
language = "English",
volume = "234",
pages = "636--648",
journal = "Journal of Computational and Applied Mathematics",
issn = "0377-0427",
number = "3",
}