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SUMMARY:Reduced modelling and approximation from point evaluations
DTSTART:20230223T144000
DTEND:20230223T153000
DTSTAMP:20260407T164034Z
UID:05588c1b4771bf2f1e27041bd643694826006b2590d5502be0f61b6a
CATEGORIES:Conferences - Seminars
DESCRIPTION:Matthieu Dolbeault – Sorbonne Université\, Paris\nTalk in M
 athematics\nIn approximation of functions\, one often seeks a reduced mode
 l\, that is\, a linear space of moderate dimension that best approaches th
 e considered class of functions. Once such a space is fixed\, the question
  remains as to how to reconstruct a given function in this space\, based o
 n a limited number of measurements. In particular\, when only point evalua
 tions can be queried\, we show that weighted least-squares can achieve nea
 r-optimal recovery error\, with a sample size close to the reduced model d
 imension. These results are obtained through a combination of randomized s
 ampling strategies andlinear algebra for sums of rank-one matrices.\n 
LOCATION:MA B1 524 https://plan.epfl.ch/?room==MA%20B1%20524 https://epfl.
 zoom.us/j/63739487991
STATUS:CONFIRMED
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