Zobrazeno 1 - 10
of 41
pro vyhledávání: '"Alexander Stollenwerk"'
Autor:
Karp, H.-J.
Publikováno v:
Annalen des Historischen Vereins für den Niederrhein; December 1972, Vol. 174 Issue: 1 p260-264, 5p
Autor:
H.-J. Karp
Publikováno v:
Annalen des Historischen Vereins für den Niederrhein. 174:260-264
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b530cb20d03215914f88f80cb5852eaa
https://doi.org/10.7788/annalen-1972-jg38
https://doi.org/10.7788/annalen-1972-jg38
Autor:
Martin Genzel, Alexander Stollenwerk
Publikováno v:
Foundations of Computational Mathematics.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::47aa97480781218be2a844ce6da4ab92
https://doi.org/10.1007/s10208-022-09562-y
https://doi.org/10.1007/s10208-022-09562-y
Publikováno v:
IEEE Transactions on Information Theory, Vol. 67, no.6, p. 4125-4149 (2021)
This work is concerned with the problem of recovering high-dimensional signals $\mathbf{x} \in \mathbb{R}^n$ which belong to a convex set of low-complexity from a small number of quantized measurements. We propose to estimate the signals via a convex
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1b2840bb916cc5dded0ee28417a4a587
https://doi.org/10.1002/pamm.202000015
https://doi.org/10.1002/pamm.202000015
Autor:
Alexander Stollenwerk, Martin Genzel
This work theoretically studies the problem of estimating a structured high-dimensional signal $\boldsymbol{x}_0 \in{\mathbb{R}}^n$ from noisy $1$-bit Gaussian measurements. Our recovery approach is based on a simple convex program which uses the hin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::681208f827a5bd760d645b5dbabdc3aa
http://arxiv.org/abs/1804.04846
http://arxiv.org/abs/1804.04846
Autor:
Alexander Stollenwerk, Martin Genzel
Publikováno v:
Information and Inference: A Journal of the IMA. 9:505-506
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::78f1e816b34de27cb30d2b1122bfac57
https://doi.org/10.1093/imaiai/iaz024
https://doi.org/10.1093/imaiai/iaz024
Autor:
Alexander Stollenwerk, Sjoerd Dirksen
Publikováno v:
2017 International Conference on Sampling Theory and Applications (SampTA).
We consider the problem of encoding a finite set of vectors into a small number of bits while approximately retaining information on the angular distances between the vectors. By deriving improved variance bounds related to binary Gaussian circulant
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::628170e1f10f0e7f915bcacf139753f2
https://doi.org/10.1109/sampta.2017.8024404
https://doi.org/10.1109/sampta.2017.8024404
Autor:
Sjoerd Dirksen, Alexander Stollenwerk
Publikováno v:
Discrete and Computational Geometry, 60(3), 599. Springer New York
We consider the problem of encoding a finite set of vectors into a small number of bits while approximately retaining information on the angular distances between the vectors. By deriving improved variance bounds related to binary Gaussian circulant
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cfb4927bcc5b623d1995d6e03a7aed31
https://doi.org/10.1007/s00454-017-9964-x
https://doi.org/10.1007/s00454-017-9964-x
Autor:
Genzel, Martin1 (AUTHOR) m.genzel@uu.nl, Stollenwerk, Alexander2 (AUTHOR)
Publikováno v:
Foundations of Computational Mathematics. Jun2023, Vol. 23 Issue 3, p899-972. 74p.
Autor:
Alexander Stollenwerk
Publikováno v:
Kommunale Finanzen und Kommunale Wirtschaft ISBN: 9783540024057
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7b27040b6c8b332e60e02b7668f360e7
https://doi.org/10.1007/978-3-642-86963-1_5
https://doi.org/10.1007/978-3-642-86963-1_5