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

• Published in: Journal of scientific computing Vol. 87; no. 1
• Main Author: Lestandi, Lucas
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2021; Springer Verlag
• Subjects: 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

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...