Entrywise tensor-train approximation of large tensors via random embeddings

Fuente: arXiv
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Auteur principal: Budzinskiy, Stanislav
Format: Preprint
Publié: 2024
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author Budzinskiy, Stanislav
author_facet Budzinskiy, Stanislav
contents The theory of low-rank tensor-train approximation is well understood when the approximation error is measured in the Frobenius norm. The entrywise maximum norm is equally important but is significantly weaker for large tensors, making the estimates obtained via the Frobenius norm and norm equivalence pessimistic or even meaningless. In this article, we derive a direct estimate of the entrywise approximation error that is applicable in some of these cases. The estimate is given in terms of the higher-order generalization of the matrix factorization norm, and its proof is based on the tensor-structured Hanson--Wright inequality. The theoretical results are accompanied by numerical experiments carried out with the method of alternating projections.
format Preprint
id arxiv_https___arxiv_org_abs_2403_11768
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Entrywise tensor-train approximation of large tensors via random embeddings
Budzinskiy, Stanislav
Numerical Analysis
Probability
The theory of low-rank tensor-train approximation is well understood when the approximation error is measured in the Frobenius norm. The entrywise maximum norm is equally important but is significantly weaker for large tensors, making the estimates obtained via the Frobenius norm and norm equivalence pessimistic or even meaningless. In this article, we derive a direct estimate of the entrywise approximation error that is applicable in some of these cases. The estimate is given in terms of the higher-order generalization of the matrix factorization norm, and its proof is based on the tensor-structured Hanson--Wright inequality. The theoretical results are accompanied by numerical experiments carried out with the method of alternating projections.
title Entrywise tensor-train approximation of large tensors via random embeddings
topic Numerical Analysis
Probability
url https://arxiv.org/abs/2403.11768