Naslov
Randomized Methods for Recompression of Low-rank Tensors

Autori
Periša Lana ; Kressner, Daniel

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, ostalo

Izvornik
SIAM CSE19 - Program and Abstracts / - , 2018, 680-680

Skup
SIAM Conference on Computational Science and Engineering (CSE19)

Mjesto i datum
Spokane (WA), Sjedinjene Američke Države, 25.02.2018. - 01.03.2018

Vrsta sudjelovanja
Poster

Vrsta recenzije
Nije recenziran

Ključne riječi
tensors, recompression, randomized algorithm

Sažetak
Many basic linear algebra operations with low-rank tensors, like addition, matrix-vector multiplication or element-wise product, have a tendency to significantly increase the rank, even though the resulting tensors admit a good low-rank approximation. We use randomized algorithm to recompress these tensors when dealing with low-rank formats such as Tucker and Tensor Train, by employing random vectors with rank-1 structure which matches the structure of the tensors. In case of element-wise product of tensors, this has shown to significantly reduce the computational effort, while achieving a similar accuracy as the corresponding deterministic techniques.

Izvorni jezik
Engleski

Znanstvena područja
Matematika

POVEZANOST RADA