Randomized algorithms for the low multilinear rank approximations of tensors
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Article number | 113380 |
| Journal / Publication | Journal of Computational and Applied Mathematics |
| Volume | 390 |
| Online published | 18 Jan 2021 |
| Publication status | Published - Jul 2021 |
Link(s)
Abstract
In this paper, we focus on developing randomized algorithms for the computation of low multilinear rank approximations of tensors based on the random projection and the singular value decomposition. Following the theory of the singular values of sub-Gaussian matrices, we make a probabilistic analysis for the error bounds for the randomized algorithm. We demonstrate the effectiveness of proposed algorithms via several numerical examples.
Research Area(s)
- Low multilinear rank approximation, Randomized algorithms, Singular value decomposition, Singular values, Sub-Gaussian matrices
Citation Format(s)
Randomized algorithms for the low multilinear rank approximations of tensors. / Che, Maolin; Wei, Yimin; Yan, Hong.
In: Journal of Computational and Applied Mathematics, Vol. 390, 113380, 07.2021.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
