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Journal Articles SIAM Journal on Scientific Computing Year : 2021

Adaptive hierarchical subtensor partitioning for tensor compression

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Abstract

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.
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Dates and versions

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

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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⟩
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