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SUMMARY:S.P. Tsarev (SFU\, Krasnoyarsk\, Russia)
DTSTART:20241017T110000Z
DTEND:20241017T120000Z
DTSTAMP:20260423T022839Z
UID:mmandim/80
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/mmandim/80/"
 >Sparse recovery and Compressive sensing in theory and in practice</a>\nby
  S.P. Tsarev (SFU\, Krasnoyarsk\, Russia) as part of Mathematical models a
 nd integration methods\n\n\nAbstract\nIn the 1990's\, algorithms for solvi
 ng linear systems with the number of equations smaller than the number of 
 unknowns\, provided that among the unknowns there are only a small number 
 of non-zero ones (however\, we do not know which of them are non-zero!) we
 re proposed.\n\nA new stage was opened in the early 2000's by the well-kno
 wn specialist in signal processing David Donoho and the Fields Medal winne
 r Terence Tao and their students. The results in this area were awarded th
 e 2018 Gauss Prize (given by the International Mathematical Union)\, they 
 were reported as plenary talks at the International Congress of Mathematic
 ians\, etc.\n\nAfter the works of Donoho\, Tao and many other researchers\
 , progress in this area was rapid. This research area was called "compress
 ive sensing" or "compressed sensing" (along with the older name "sparse re
 covery").\n\nThe most well-known applications of these results are in sign
 al processing. Particularly noteworthy are applications of sparse recovery
  technologies in magnetic resonance imaging (MRI)\, which reduce the time 
 spend by patients in the MRI machine and improve the quality of the result
 ing image.\n\nThe report will discuss the main ideas of this area and demo
 nstrate a small practical application in the problem of finding jumps in a
  noisy signal.\n
LOCATION:https://researchseminars.org/talk/mmandim/80/
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