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Pencil-Based Algorithms For Tensor Rank Decomposition Are Not Stable

Abstract: We prove the existence of an open set of n1 ×n2 ×n3 tensors of rank r for which popular and e?cient algorithms for computing tensor rank decompositions based on a reduction to a linear matrix pencil, typically followed by a generalized eigendecomposition, are arbitrarily numerically forward unstable. Our analysis shows that this problem is caused by the fact that the condition number of tensor rank decomposition can be much larger for n1 ×n2 ×2 tensors than for the n1 ×n2 ×n3 input tensor. Moreover, we present a lower bound for the limiting distribution of the condition number of random tensor rank decompositions of third-order tensors. The numerical experiments illustrate that for random tensor rank decompositions one should anticipate a loss of precision of a few digits.

Otras publicaciones de la misma revista o congreso con autores/as de la Universidad de Cantabria

 Fuente: SIAM Journal on Matrix Analysis and Applications Vol. 40, No. 2

Editorial: Society for Industrial and Applied Mathematics

 Año de publicación: 2019

Nº de páginas: 35

Tipo de publicación: Artículo de Revista

 DOI: 10.1137/18M1200531

ISSN: 0895-4798,1095-7162

 Proyecto español: MTM2017-83816-P ; MTM2017-90682-REDT

Url de la publicación: https://doi.org/10.1137/18M1200531

Autoría

BREIDING, PAUL

VANNIEUWENHOVEN, NICK