Publications
Article · Linear and Multilinear Algebra · March 3, 2024
Probabilistic bounds on best rank-1 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/nd−12, 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/nd−12.
Tensor decomposition and applicationsSparse and Compressive Sensing TechniquesMathematical Approximation and Integrationsymmetric tensorsspectral normFrobenius norm
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