Randomized algorithms for the low multilinear rank approximations of tensors

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Article number113380
Journal / PublicationJournal of Computational and Applied Mathematics
Online published18 Jan 2021
Publication statusPublished - Jul 2021


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