Efficient Analysis of High Dimensional Data in Tensor Formats
By M. Espig, Wolfgang Hackbusch, Alexander Litvinenko, Hermann G. Matthies, Elmar Zander. 2012
78 citations recorded by the source corpus.
Subfields: Atomic and Molecular Physics, and Optics, Computational Mechanics, Statistics and Probability
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Atomic and Molecular Physics, and Optics
- M. Espig
- Alexander Litvinenko
- Wolfgang Hackbusch
- Hermann G. Matthies
- André Uschmajew
- Philipp Wähnert
- Boris N. Khoromskij
- Lars Grasedyck
In Computational Mechanics
- André Uschmajew
- Wolfgang Hackbusch
- Alexander Litvinenko
- M. Espig
- Hermann G. Matthies
- Thorsten Rohwedder
- Christine Tobler
- Daniel Kressner
In Statistics and Probability
- Wolfgang Hackbusch
- M. Espig
- Alexander Litvinenko
- Philipp Wähnert
- André Uschmajew
- Hermann G. Matthies
- Anthony Nouy
- Christoph Schwab
Citations in the corpus
- A literature survey of low‐rank tensor approximation techniques (2013, neutral): “• the solution of stochastic and parametric PDEs [69, 70, 77, 78, 162, 166, 172, 190, 203];”
- Zur Theorie der Niedrigrangapproximation in Tensorprodukten von Hilberträumen (2013, background): “[23, 24], Hackbusch [34], Kühn [59], sowie Beylkin u.”
- Efficient low-rank approximation of the stochastic Galerkin matrix in tensor formats (2014, background): “The mean and covariance functions of κ determine the mean and covariance functions of γ entirely, since Γγ(x, y) = ln [Γκ(x, y) +mκ(x)mκ(y)]− ln [mκ(x)mκ(y)] , (3) mγ(x) = 2 lnmκ(x)− 12 ln [ Γκ(x, x) +mκ(x) 2 ] .”