Randomized algorithms for the approximations of Tucker and the tensor train decompositions

Randomized algorithms provide a powerful tool for scientific computing. Compared with standard deterministic algorithms, randomized algorithms are often faster and robust. The main purpose of this paper is to design adaptive randomized algorithms for computing the approximate tensor decompositions....

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Bibliographic Details
Published in:	Advances in computational mathematics Vol. 45; no. 1; pp. 395 - 428
Main Authors:	Che, Maolin, Wei, Yimin
Format:	Journal Article
Language:	English
Published:	New York Springer US 05.02.2019 Springer Nature B.V
Subjects:	Adaptive algorithms Algorithms Computation Computational mathematics Computational Mathematics and Numerical Analysis Computational Science and Engineering Decomposition Error analysis Mathematical analysis Mathematical and Computational Biology Mathematical Modeling and Industrial Mathematics Mathematics Mathematics and Statistics Randomization Robustness (mathematics) Software Tensors Visualization Kronecker structures Multilinear rank TT-approximation TT-rank 15A69 Adaptive randomized algorithms Tensor train decomposition 15A18 65F10 Low multilinear rank approximation Randomized algorithms 65F15 Tucker decomposition
ISSN:	1019-7168, 1572-9044
Online Access:	Get full text
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