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UID:20251021T170933EDT-7976oCMDUu@132.216.98.100
DTSTAMP:20251021T210933Z
DESCRIPTION:Title: The role of random models in compressive sensing and mat
 rix completion\n\nAbstract: Random models lead to a precise and comprehens
 ive theory of compressive sensing and matrix completion. The number of ran
 dom linear measurements needed to recover a sparse signal\, or a low-rank 
 matrix\, or\, more generally\, a structured signal\, are now well understo
 od. Indeed\, this boils down to a question in random matrix theory: How we
 ll conditioned is a random matrix restricted to a fixed subset of R^n? We 
 discuss recent work addressing this question in the sub-Gaussian case. Nev
 ertheless\, a practitioner with a fixed data set will wonder: Can they app
 ly theory based on randomness? Is there any hope to get the same guarantee
 s? We discuss these questions in compressive sensing and matrix completion
 \, which\, surprisingly\, seem to have divergent answers.\n
DTSTART:20191115T210000Z
DTEND:20191115T220000Z
LOCATION:Room 6214\, CA\, Pav. André-Aisenstadt
SUMMARY:Lecture by Yaniv Plan\, 2019 André-Aisenstadt Prize Recipient
URL:/mathstat/channels/event/lecture-yaniv-plan-2019-a
 ndre-aisenstadt-prize-recipient-302421
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