Abstract
The software contains a MATLAB implementation of the Generalized Preconditioned Locally Harmonic Residual (GPLHR) method for solving standard and generalized non-Hermitian eigenproblems. The method is particularly useful for computing a subset of eigenvalues, and their eigen- or Schur vectors, closest to a given shift. The proposed method is based on block iterations and can take advantage of a preconditioner if it is available. It does not need to perform exact shift-and-invert transformation. Standard and generalized eigenproblems are handled in a unified framework.
- Developers:
- Release Date:
- 2017-04-25
- Project Type:
- Open Source, No Publicly Available Repository
- Software Type:
- Scientific
- Licenses:
-
Other (Commercial or Open-Source): https://ipo.lbl.gov/marketplace
- Sponsoring Org.:
-
USDOEPrimary Award/Contract Number:AC02-05CH11231
- Code ID:
- 57356
- Site Accession Number:
- 7467; 2017-055
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Country of Origin:
- United States
Citation Formats
Vecharynski, Eugene, and Yang, Chao.
Generalized Preconditioned Locally Harmonic Residual Eigensolver (GPLHR) v0.1.
Computer Software.
USDOE.
25 Apr. 2017.
Web.
doi:10.11578/dc.20210521.109.
Vecharynski, Eugene, & Yang, Chao.
(2017, April 25).
Generalized Preconditioned Locally Harmonic Residual Eigensolver (GPLHR) v0.1.
[Computer software].
https://doi.org/10.11578/dc.20210521.109.
Vecharynski, Eugene, and Yang, Chao.
"Generalized Preconditioned Locally Harmonic Residual Eigensolver (GPLHR) v0.1." Computer software.
April 25, 2017.
https://doi.org/10.11578/dc.20210521.109.
@misc{
doecode_57356,
title = {Generalized Preconditioned Locally Harmonic Residual Eigensolver (GPLHR) v0.1},
author = {Vecharynski, Eugene and Yang, Chao},
abstractNote = {The software contains a MATLAB implementation of the Generalized Preconditioned Locally Harmonic Residual (GPLHR) method for solving standard and generalized non-Hermitian eigenproblems. The method is particularly useful for computing a subset of eigenvalues, and their eigen- or Schur vectors, closest to a given shift. The proposed method is based on block iterations and can take advantage of a preconditioner if it is available. It does not need to perform exact shift-and-invert transformation. Standard and generalized eigenproblems are handled in a unified framework.},
doi = {10.11578/dc.20210521.109},
url = {https://doi.org/10.11578/dc.20210521.109},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20210521.109}},
year = {2017},
month = {apr}
}