# On orthogonal tensors and best rank-one approximation ratio

arXiv: https://arxiv.org/abs/1707.02569 · HTML: https://mlsanity.com/paper/1707.02569/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2017.

## Built on by (most cited first, 2 of 2)

- [Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Estimation from Incomplete Measurements](https://mlsanity.com/paper/2104.14526/) (2021)
- [Self-Reflective Terrain-Aware Robot Adaptation for Consistent Off-Road Ground Navigation](https://mlsanity.com/paper/2111.06742/) (2021)

## Abstract (arXiv)

As is well known, the smallest possible ratio between the spectral norm and the Frobenius norm of an m × n matrix with m ≤ n is 1/√m and is (up to scalar scaling) attained only by matrices having pairwise orthonormal rows. In the present paper, the smallest possible ratio between spectral and Frobenius norms of n1 × dots × nd tensors of order d, also called the best rank-one approximation ratio in the literature, is investigated. The exact value is not known for most configurations of n1 ≤ dots ≤ nd. Using a natural definition of orthogonal tensors over the real field (resp., unitary tensors over the complex field), it is shown that the obvious lower bound 1/√n1 cdots nd-1 is attained if and only if a tensor is orthogonal (resp., unitary) up to scaling. Whether or not orthogonal or unitary tensors exist depends on the dimensions n1,dots,nd and the field. A connection between the (non)existence of real orthogonal tensors of order three and the classical Hurwitz problem on composition algebras can be established: existence of orthogonal tensors of size ℓ × m × n is equivalent to the admissibility of the triple [ℓ,m,n] to the Hurwitz problem. Some implications for higher-order tensors are then given. For instance, real orthogonal n × dots × n tensors of order d ≥ 3 do exist, but only when n = 1,2,4,8. In the complex case, the situation is more drastic: unitary tensors of size ℓ × m × n with ℓ ≤ m ≤ n exist only when ℓ m ≤ n. Finally, some numerical illustrations for spectral norm computation are presented.