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UID:566@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20150130T140000
DTEND;TZID=Europe/Paris:20150130T150000
DTSTAMP:20240524T072505Z
URL:https://www.i2m.univ-amu.fr/evenements/l-condat-gipsa-lab-a-new-primal
 -dual-splitting-algorithm-for-convex-optimization-application-as-a-heurist
 ic-for-super-resolution-2/
SUMMARY: (...): L. Condat (GIPSA-lab) : A new primal-dual splitting algorit
 hm for convex optimization\; application as a heuristic for super-resoluti
 on
DESCRIPTION:: Summary: A new splitting algorithm is proposed to minimize th
 e sum ofnconvex functions\, potentially nonsmooth and composed with linear
 noperators. This generic formulation encompasses numerous regularizedninve
 rse problems in image processing. The algorithm\, whose weaknconvergence i
 s proved\, calls the individual gradient or proximitynoperators of the fun
 ctions\, without any inner loop or linear system tonsolve. The classical D
 ouglas-Rachford\, forward-backward andnChambolle-Pock algorithms are recov
 ered as particular cases. In thensecond part of the talk (joint work with 
 A. Hirabayashi\, Kyoto\, Japan)\,nI address the problem of recovering a sp
 ike train from noisy lowpassnmeasurements\, through a reformulation as a s
 tructured low rank matrixnapproximation problem. Used as a heuristic for t
 his nonconvex problem\,nthe proposed algorithm yields state-of-the-art res
 ults.
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