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SUMMARY:Compressive Sensing with Structured Random Matrices - Holger Rauhu
 t (Aachen University)
DTSTART:20170309T150000Z
DTEND:20170309T160000Z
UID:TALK70142@talks.cam.ac.uk
CONTACT:Carola-Bibiane Schoenlieb
DESCRIPTION:Compressive sensing predicts that sparse vectors can be recove
 red via efficient algorithms from what was previously believed to be incom
 plete information.\nRecovery methods include convex optimization approache
 s (l1-minimization). Provably optimal measurement process are described vi
 a Gaussian random matrices.\nIn practice\, however\, more structure is req
 uired. We describe the state of the art on recovery results for several ty
 pes of structured random measurement matrices\, including random partial F
 ourier matrices and subsampled random convolutions.
LOCATION:MR 14\, CMS
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