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Probabilistic bounds on best rank-one approximation ratio

Khazhgali Kozhasov, Josué Tonelli-Cueto

Abstract

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 $1/\sqrt{n^{d-1}}$.

Probabilistic bounds on best rank-one approximation ratio

Abstract

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 , when the order of a tensor is fixed and the dimension of the underlying vector space tends to infinity. However, when is fixed and tends to infinity, our lower bound is better than .
Paper Structure (19 sections, 18 theorems, 145 equations)

This paper contains 19 sections, 18 theorems, 145 equations.

Key Result

Theorem 1.1

For any $d\geq 3$ and $\boldsymbol{n}=(n_1,\dots, n_d)$ with $n_1,\dots, n_d\geq 2$ we have

Theorems & Definitions (40)

  • Theorem 1.1
  • Theorem 1.2
  • Remark 1.3
  • Theorem 1.4
  • Lemma 2.1
  • proof
  • Proposition 3.1
  • Remark 3.2
  • Remark 3.3
  • Proposition 3.4
  • ...and 30 more