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SUMMARY: Iterative Algorithms in Compressive Sensing - Simon Foucart (Drex
 el University)
DTSTART:20130319T150000Z
DTEND:20130319T160000Z
UID:TALK43917@talks.cam.ac.uk
CONTACT:Carola-Bibiane Schoenlieb
DESCRIPTION:Over the past few years\, $\\ell_1$-minimization has become th
 e most popular method to recover sparse vectors from incomplete linear mea
 surements. \nHowever\, simpler iterative algorithms such as Iterative Hard
  Thresholding present the same theoretical guarantees when the measurement
  matrix satisfies the restricted isometry property. \nIn this talk\, I wil
 l focus on iterative algorithms that converge in a finite number of iterat
 ions proportional to the sparsity level.\nThis fact was observed only rece
 ntly for (weak) Orthogonal Matching Pursuit.\nI will also demonstrate a si
 milar result for Hard Thresholding Pursuit. Advantages of the latter algor
 ithm will be recalled along the way.
LOCATION:MR 14\, CMS
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