Numerical Study of Low Rank Approximation Methods for Mechanics Data and Its Analysis

This paper proposes a comparison of the numerical aspect and efficiency of several low rank approximation techniques for multidimensional data, namely CPD, HOSVD, TT-SVD, RPOD, QTT-SVD and HT. This approach is different from the numerous papers that compare the theoretical aspects of these methods o...

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Veröffentlicht in:	Journal of scientific computing Jg. 87; H. 1
1. Verfasser:	Lestandi, Lucas
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.04.2021 Springer Verlag
Schlagworte:	Algorithms Computational Mathematics and Numerical Analysis Computer Science Data Analysis, Statistics and Probability Mathematical and Computational Engineering Mathematical and Computational Physics Mathematics Mathematics and Statistics Mechanics Modeling and Simulation Physics Theoretical Tensor decomposition Hierarchical SVD HOSVD POD Canonical decomposition 15A69 Tensor train HT 78M34 QTT RPOD 35Q35 15A21 ST-HOSVD Low rank approximation Canonical Decomposition Tensor Train SVD Mathematics Subject Classification 35Q35 tensor decomposition
ISSN:	0885-7474, 1573-7691
Online-Zugang:	Volltext
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