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| 001 | 811979 | ||
| 005 | 20221109161713.0 | ||
| 024 | 7 | _ | |2 doi |a 10.1002/nla.2048 |
| 024 | 7 | _ | |2 WOS |a WOS:000383673200006 |
| 037 | _ | _ | |a FZJ-2016-04280 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 510 |
| 100 | 1 | _ | |0 P:(DE-Juel1)144723 |a Di Napoli, Edoardo |b 0 |e Corresponding author |u fzj |
| 245 | _ | _ | |a Efficient estimation of eigenvalue counts in an interval |
| 260 | _ | _ | |a New York, NY [u.a.] |b Wiley |c 2016 |
| 336 | 7 | _ | |2 DRIVER |a article |
| 336 | 7 | _ | |2 DataCite |a Output Types/Journal article |
| 336 | 7 | _ | |0 PUB:(DE-HGF)16 |2 PUB:(DE-HGF) |a Journal Article |b journal |m journal |s 1470914832_13979 |
| 336 | 7 | _ | |2 BibTeX |a ARTICLE |
| 336 | 7 | _ | |2 ORCID |a JOURNAL_ARTICLE |
| 336 | 7 | _ | |0 0 |2 EndNote |a Journal Article |
| 520 | _ | _ | |a Estimating the number of eigenvalues located in a given interval of a large sparse Hermitian matrix is an important problem in certain applications, and it is a prerequisite of eigensolvers based on a divide-and-conquer paradigm. Often, an exact count is not necessary, and methods based on stochastic estimates can be utilized to yield rough approximations. This paper examines a number of techniques tailored to this specific task. It reviews standard approaches and explores new ones based on polynomial and rational approximation filtering combined with a stochastic procedure. We also discuss how the latter method is particularly well- suited for the FEAST eigensolver. |
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| 536 | _ | _ | |a Simulation and Data Laboratory Quantum Materials (SDLQM) (SDLQM) |0 G:(DE-Juel1)SDLQM |c SDLQM |f Simulation and Data Laboratory Quantum Materials (SDLQM) |x 2 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Polizzi, Eric |b 1 |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Saad, Yousef |b 2 |
| 773 | _ | _ | |0 PERI:(DE-600)2012602-5 |a 10.1002/nla.2048 |n 4 |p 674-692 |t Numerical linear algebra with applications |v 23 |x 1070-5325 |y 2016 |
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| 914 | 1 | _ | |y 2016 |
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