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Main Authors: Kozhasov, Khazhgali, Tonelli-Cueto, Josué
Format: Preprint
Published: 2022
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Online Access:https://arxiv.org/abs/2201.02191
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author Kozhasov, Khazhgali
Tonelli-Cueto, Josué
author_facet Kozhasov, Khazhgali
Tonelli-Cueto, Josué
contents We provide new upper and lower bounds on the minimum possible ratio of the spectral and Frobenius norms of a (partially) symmetric tensor. In the particular case of general tensors our result recovers a known upper bound. For symmetric tensors our upper bound unveils that the ratio of norms has the same order of magnitude as the trivial lower bound $1/\sqrt{n^{d-1}}$, when the order of a tensor $d$ is fixed and the dimension of the underlying vector space $n$ tends to infinity. However, when $n$ is fixed and $d$ tends to infinity, our lower bound is better than $1/\sqrt{n^{d-1}}$.
format Preprint
id arxiv_https___arxiv_org_abs_2201_02191
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle Probabilistic bounds on best rank-one approximation ratio
Kozhasov, Khazhgali
Tonelli-Cueto, Josué
Functional Analysis
Optimization and Control
Probability
15A69, 26C05, 41A50
We provide new upper and lower bounds on the minimum possible ratio of the spectral and Frobenius norms of a (partially) symmetric tensor. In the particular case of general tensors our result recovers a known upper bound. For symmetric tensors our upper bound unveils that the ratio of norms has the same order of magnitude as the trivial lower bound $1/\sqrt{n^{d-1}}$, when the order of a tensor $d$ is fixed and the dimension of the underlying vector space $n$ tends to infinity. However, when $n$ is fixed and $d$ tends to infinity, our lower bound is better than $1/\sqrt{n^{d-1}}$.
title Probabilistic bounds on best rank-one approximation ratio
topic Functional Analysis
Optimization and Control
Probability
15A69, 26C05, 41A50
url https://arxiv.org/abs/2201.02191