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1 Ergebnisse
1
Krylov Methods are (nearly) Optimal for Low-Rank Approximat..:
, In:
2023 IEEE 64th Annual Symposium on Foundations of Computer Science (FOCS)
,
Bakshi, Ainesh
;
Narayanan, Shyam
- p. 2093-2101 , 2023
Link:
https://doi.org/10.1109/FOCS57990.2023.00128
RT T1
2023 IEEE 64th Annual Symposium on Foundations of Computer Science (FOCS)
: T1
Krylov Methods are (nearly) Optimal for Low-Rank Approximation
UL https://suche.suub.uni-bremen.de/peid=ieee-10353123&Exemplar=1&LAN=DE A1 Bakshi, Ainesh A1 Narayanan, Shyam YR 2023 SN 2575-8454 K1 Computer science K1 Upper bound K1 Computational modeling K1 Approximation algorithms K1 Complexity theory K1 Matrix-vector model K1 sublinear algorithms K1 low-rank approximation SP 2093 OP 2101 LK http://dx.doi.org/https://doi.org/10.1109/FOCS57990.2023.00128 DO https://doi.org/10.1109/FOCS57990.2023.00128 SF ELIB - SuUB Bremen
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