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SUMMARY:Randomized methods for low-rank approximation of matrices and tens
 ors - Yuji Nakatsukasa (University of Oxford)
DTSTART:20231012T140000Z
DTEND:20231012T150000Z
UID:TALK206758@talks.cam.ac.uk
CONTACT:Nicolas Boulle
DESCRIPTION:Among the most exciting recent developments in numerical linea
 r algebra is the advent of randomized algorithms that are fast\, scalable\
 , robust\, and reliable.\nLow-rank approximation is among the most signifi
 cant problems for which randomization has had a significant impact.\nIn th
 is talk I will first review some of the most successful randomized algorit
 hms for low-rank approximation of matrices. I will then turn to tensors\, 
 and describe an algorithm RTSMS (Randomized Tucker with single-mode sketch
 ing) for an approximate Tucker decomposition. RTSMS only sketches one mode
  at a time\, so the sketch matrices are significantly smaller than alterna
 tive approaches\, and RTSMS can outperform existing methods by a large mar
 gin. RTSMS is a joint work with Behnam Hashemi (Leicester).
LOCATION:Centre for Mathematical Sciences\, MR14
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