Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Ronald Kriemann"'
Publikováno v:
MethodsX, Vol 7, Iss , Pp 100600- (2020)
We provide more technical details about the HLIBCov package, which is using parallel hierarchical (H-) matrices to: • Approximate large dense inhomogeneous covariance matrices with a log-linear computational cost and storage requirement. • Comput
Externí odkaz:
https://doaj.org/article/7ffa986062164ecfa17241beeaa628de
Publikováno v:
SIAM Journal on Scientific Computing. 44:C77-C98
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2e58812c82bbd3a5992303271429105d
https://doi.org/10.1137/20m1318808
https://doi.org/10.1137/20m1318808
Autor:
Ronald Kriemann, Hatem Ltaief, Minh Bau Luong, Francisco E. Hernández Pérez, Hong G. Im, David Keyes
Publikováno v:
Euro-Par 2022: Parallel Processing ISBN: 9783031125966
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9d91cc5796ed15fd9048b7ed5c3a99fb
https://doi.org/10.1007/978-3-031-12597-3_25
https://doi.org/10.1007/978-3-031-12597-3_25
Publikováno v:
PAMM. 21
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7a0ced27a2f180234dc368f11ba5c995
https://doi.org/10.1002/pamm.202100043
https://doi.org/10.1002/pamm.202100043
Statistical analysis of massive datasets very often implies expensive linear algebra operations with large dense matrices. Typical tasks are an estimation of unknown parameters of the underlying statistical model and prediction of missing values. We
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c7df54faafe28c500933b53e0e197db1
http://arxiv.org/abs/2104.07146
http://arxiv.org/abs/2104.07146
We introduce a novel eigenvalue algorithm for near-diagonal matrices inspired by Rayleigh-Schr\"odinger perturbation theory and termed Iterative Perturbative Theory (IPT). Contrary to standard eigenvalue algorithms, which are either 'direct' (to comp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::386052719bf4f4e4a1879daf4633a0ef
Publikováno v:
MethodsX
MethodsX 7, 100600 (2020). doi:10.1016/j.mex.2019.07.001
MethodsX, Vol 7, Iss, Pp 100600-(2020)
MethodsX 7, 100600 (2020). doi:10.1016/j.mex.2019.07.001
MethodsX, Vol 7, Iss, Pp 100600-(2020)
Graphical abstract
We provide more technical details about the HLIBCov package, which is using parallel hierarchical (H-) matrices to: • Approximate large dense inhomogeneous covariance matrices with a log-linear computational cost and storage
We provide more technical details about the HLIBCov package, which is using parallel hierarchical (H-) matrices to: • Approximate large dense inhomogeneous covariance matrices with a log-linear computational cost and storage
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ed1793ec461f71c0d022adb4507d0389
http://europepmc.org/articles/PMC6992995
http://europepmc.org/articles/PMC6992995
Publikováno v:
Adaptive Optics Systems VI.
Implementations of AO tomography for the next generation of Extremely Large Telescopes (ELTs) is challenging because of the extremely large number of degrees of freedom of such systems, in particular when it comes to the tomographic reconstructor com
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::703e1d21e38c4a5f8c34591173cc7ce6
https://doi.org/10.1117/12.2313273
https://doi.org/10.1117/12.2313273
Autor:
S. Le Borne, Ronald Kriemann
Publikováno v:
Computing and Visualization in Science. 17:135-150
Given a sparse matrix, its LU-factors, inverse and inverse factors typically suffer from substantial fill-in, leading to non-optimal complexities in their computation as well as their storage. In the past, several computationally efficient methods ha
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f9e6e1f2a9acfbbbfd4c8e6a4a9ca27a
https://doi.org/10.1007/s00791-015-0254-y
https://doi.org/10.1007/s00791-015-0254-y
Autor:
Ronald Kriemann, Lars Grasedyck, Konstantinos Xylouris, Gabriel Wittum, Christian Löbbert, Arne Nägel
Publikováno v:
Computing and Visualization in Science. 17:67-78
We consider the problem of uncertainty quantification for extreme scale parameter dependent problems where an underlying low rank property of the parameter dependency is assumed. For this type of dependency the hierarchical Tucker format offers a sui
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6f3082be20fa2b718f4918c12008ce24
https://doi.org/10.1007/s00791-015-0247-x
https://doi.org/10.1007/s00791-015-0247-x