Naslov
Randomized Rank-1 Method for Tensor Recompression

Autori
Periša Lana ; Kressner Daniel

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

Izvornik
ICIAM 2019 - program and abstract book / - , 2019, 272-272

Skup
The 9th International Congress on Industrial and Applied Mathematics (ICIAM 2019)

Mjesto i datum
Valencia, Španjolska, 15.07.2019. - 19.07.2019

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Nije recenziran

Ključne riječi
tensors, recompression, randomized algorithm

Sažetak
Many basic linear algebra operations with low-rank tensors, like 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, which has shown to significantly reduce the computational effort

Izvorni jezik
Engleski

Znanstvena područja
Matematika

POVEZANOST RADA