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On orthogonal tensors and best rank-one approximation ratio

2017/07/09 by Zhening Li, Li, Zhening, Yuji Nakatsukasa +5
Computer Science · Mathematics · #Advanced Optimization Algorithms Research #Algebraic Geometry (math.AG) #FOS: Mathematics #Matrix Theory and Algorithms #Numerical Analysis (math.NA) #Optimization and Control (math.OC) #Tensor decomposition and applications

paper · pdf · doi:10.48550/arxiv.1707.02569

openalex publication_date 2017/07/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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 × … × 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 ≤ … ≤ 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 ⋯ 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,…,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 × … × 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.

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