Adaptive hierarchical subtensor partitioning for tensor compression - Université Paris Cité Accéder directement au contenu
Article Dans Une Revue SIAM Journal on Scientific Computing Année : 2021

Adaptive hierarchical subtensor partitioning for tensor compression

Résumé

In this work a numerical method is proposed to compress a tensor by constructing a piece-wise tensor approximation. This is defined by partitioning a tensor into sub-tensors and by computing a low-rank tensor approximation (in a given format) in each sub-tensor. Neither the partition nor the ranks are fixed a priori, but, instead, are obtained in order to fulfill a prescribed accuracy and optimize, to some extent, the storage. The different steps of the method are detailed and some numerical experiments are proposed to assess its performances.
Fichier principal
Vignette du fichier
preprint.pdf (2.12 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-02284456 , version 1 (11-09-2019)

Identifiants

Citer

Virginie Ehrlacher, Laura Grigori, Damiano Lombardi, Hao Song. Adaptive hierarchical subtensor partitioning for tensor compression. SIAM Journal on Scientific Computing, 2021, ⟨10.1137/19M128689X⟩. ⟨hal-02284456⟩
452 Consultations
616 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More