Journal
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS
Volume 390, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.cam.2020.113380
Keywords
Randomized algorithms; Low multilinear rank approximation; Sub-Gaussian matrices; Singular value decomposition; Singular values
Categories
Funding
- National Natural Science Foundation of China [11771099, 11901471]
- Shanghai Municipal Science and Technology Major Project [2018SHZDZX01]
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (LCNBI)
- ZJLab, PR China
- Innovation Program of Shanghai Municipal Education Commission, China
- Hong Kong Innovation and Technology Commission
- City University of Hong Kong [9610308]
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This paper focuses on developing randomized algorithms for computing low multilinear rank approximations of tensors using random projection and singular value decomposition. The probabilistic analysis of error bounds for the randomized algorithm is based on the theory of singular values of sub-Gaussian matrices. The effectiveness of the proposed algorithms is demonstrated through several numerical examples.
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. (C) 2021 Elsevier B.V. All rights reserved.
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