Zobrazeno 1 - 10 of 156 pro vyhledávání: '"Erich Novak"'
1
Random sections of ellipsoids and the power of random information
Autor: Joscha Prochno, David Krieg, Mario Ullrich, Erich Novak, Aicke Hinrichs
Publikováno v: Transactions of the American Mathematical Society. 374:8691-8713
We study the circumradius of the intersection of an m m -dimensional ellipsoid E \mathcal E with semi-axes σ 1 ≥ ⋯ ≥ σ m \sigma _1\geq \dots \geq \sigma _m with random subspaces of codimension n n , where n n can be much smaller than m m . We
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b5eeb4512e8854a56d84274ce505ad31
https://doi.org/10.1090/tran/8502
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6
Recovery of Sobolev functions restricted to iid sampling
Autor: David Krieg, Erich Novak, Mathias Sonnleitner
We study $L_q$-approximation and integration for functions from the Sobolev space $W^s_p(\Omega)$ and compare optimal randomized (Monte Carlo) algorithms with algorithms that can only use iid sample points, uniformly distributed on the domain. The ma
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e93768ea3fb9587d93490d3db1423d4e
http://arxiv.org/abs/2108.02055
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9
Lower bounds for integration and recovery in $L_2$
Autor: Aicke Hinrichs, David Krieg, Erich Novak, Jan Vybíral
Function values are, in some sense, "almost as good" as general linear information for $L_2$-approximation (optimal recovery, data assimilation) of functions from a reproducing kernel Hilbert space. This was recently proved by new upper bounds on the
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4bca51d98cae117dff49fd1de7b8a449
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