Randomized Rank-1 Method for Tensor Recompression (CROSBI ID 682311)
Prilog sa skupa u zborniku | sažetak izlaganja sa skupa
Podaci o odgovornosti
Periša Lana ; Kressner Daniel
engleski
Randomized Rank-1 Method for Tensor Recompression
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
tensors, recompression, randomized algorithm
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Podaci o prilogu
272-272.
2019.
objavljeno
Podaci o matičnoj publikaciji
ICIAM 2019 - program and abstract book
Podaci o skupu
The 9th International Congress on Industrial and Applied Mathematics (ICIAM 2019)
predavanje
15.07.2019-19.07.2019
Valencia, Španjolska