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pro vyhledávání: '"Jed A. Duersch"'
Autor:
Jed A. Duersch, Thomas A. Catanach
Publikováno v:
Entropy, Vol 22, Iss 1, p 108 (2020)
Information theory provides a mathematical foundation to measure uncertainty in belief. Belief is represented by a probability distribution that captures our understanding of an outcome’s plausibility. Information measures based on Shannon’s conc
Externí odkaz:
https://doaj.org/article/87846fd680544394ac06a201758453de
Publikováno v:
2022 IEEE 61st Conference on Decision and Control (CDC).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::95ae65122ffe96696d0e7f74dacf91e0
https://doi.org/10.1109/cdc51059.2022.9992586
https://doi.org/10.1109/cdc51059.2022.9992586
Autor:
Jed A. Duersch, Ming Gu
Publikováno v:
SIAM Review. 62:661-682
Rank-revealing matrix decompositions provide an essential tool in spectral analysis of matrices, including the Singular Value Decomposition (SVD) and related low-rank approximation techniques. QR with Column Pivoting (QRCP) is usually suitable for th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2430d5dee6952978d0209e84a65bdd6b
https://doi.org/10.1137/20m1335571
https://doi.org/10.1137/20m1335571
Tensor decomposition is a fundamental unsupervised machine learning method in data science, with applications including network analysis and sensor data processing. This work develops a generalized canonical polyadic (GCP) low-rank tensor decompositi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d9258879d89197f042bc90794361ebf6
http://arxiv.org/abs/1808.07452
http://arxiv.org/abs/1808.07452
Publikováno v:
SIAM Journal on Scientific Computing, vol 40, iss 5
Duersch, JA; Shao, M; Yang, C; & Gu, M. (2017). A robust and efficient implementation of LOBPCG. Lawrence Berkeley National Laboratory: Retrieved from: http://www.escholarship.org/uc/item/7c90z1hr
Duersch, JA; Shao, M; Yang, C; & Gu, M. (2017). A robust and efficient implementation of LOBPCG. Lawrence Berkeley National Laboratory: Retrieved from: http://www.escholarship.org/uc/item/7c90z1hr
Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG) is widely used to compute eigenvalues of large sparse symmetric matrices. The algorithm can suffer from numerical instability if it is not implemented with care. This is especially prob
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5727bcaf0b7e53cc1f7f22efafdf5bc3
https://escholarship.org/uc/item/7c90z1hr
https://escholarship.org/uc/item/7c90z1hr
Autor:
Thomas A. Catanach, Jed A. Duersch
Publikováno v:
Entropy, Vol 22, Iss 1, p 108 (2020)
Entropy
Volume 22
Issue 1
Entropy
Volume 22
Issue 1
Information theory provides a mathematical foundation to measure uncertainty in belief. Belief is represented by a probability distribution that captures our understanding of an outcome&rsquo
s plausibility. Information measures based on Shannon
s plausibility. Information measures based on Shannon
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::23c50c3cd0e13de1f81f034a500ab1cf
https://doi.org/10.3390/e22010108
https://doi.org/10.3390/e22010108
Autor:
Jed A. Duersch, Ming Gu
The dominant contribution to communication complexity in factorizing a matrix using QR with column pivoting is due to column-norm updates that are required to process pivot decisions. We use randomized sampling to approximate this process which drama
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::76b9f9a4be2e968afffdaab06251bb8f