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UID:2404@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20180622T140000
DTEND;TZID=Europe/Paris:20180622T150000
DTSTAMP:20180607T120000Z
URL:https://www.i2m.univ-amu.fr/evenements/cut-pursuit-algorithm-for-regul
 arizing-nonsmooth-functionals-with-graph-total-variation/
SUMMARY: (...): Cut-pursuit algorithm for regularizing nonsmooth functional
 s with graph total variation
DESCRIPTION:: I will present an extension of the cut-pursuit algorithm\, in
 troduced by Landrieu and Obozinski (2017)\, to the _graph total-variation_
  regularization of functions with a separable nondifferentiable part.We pr
 opose a modified algorithmic scheme as well as adapted proofs of convergen
 ce. We also present a heuristic approach for handling the cases in which t
 he values associated to each vertex of the graph are multidimensional.The 
 performance of our algorithm\, which we demonstrate on difficult\, ill-con
 ditioned large-scale inverse and learning problems\, is such that it may i
 n practice extend the scope of application of the total-variation regulari
 zation. -This is a joint work with Loïc Landrieu (IGN\, LaSTIG MATIS). -L
 andrieu\, L. and Obozinski\, G. "[Cut pursuit: Fast algorithms to learn pi
 ecewiseconstant functions on general weighted graphs->https://hal.archives
 -ouvertes.fr/hal-01306779v4]". SIAM Journal on ImagingSciences\, 10(4):172
 4–1766\, 2017.https://www.researchgate.net/scientific-contributions/2046
 330849_Hugo_Raguethttp://1a7r0ch3.github.io
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DTSTART:20180325T030000
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