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SUMMARY:Optimal levaraging of strong convexity and smoothness for splittin
 g algorithms
DTSTART:20260619T080000Z
DTEND:20260619T083000Z
DTSTAMP:20260626T214900Z
UID:indico-event-16777@indico.math.cnrs.fr
DESCRIPTION:Speakers: Luis Briceño-Arias\n\nIn this talk\, we establish a
  tight optimal linear convergence rates for a class of strongly convex opt
 imization problems involving two functions\, at least one of which is smoo
 th. Our approach consists of applying the classical splitting algorithms (
 including proximal-gradient\, FISTA\, and Peaceman-Rachford algorithms) to
  an equivalent modified optimization problem with adjustable levels of str
 ong convexity and smoothness. The \nobtained rate generalizes the known t
 ight rates for the case in which one function is both strongly convex and 
 smooth\, and is strictly better than the best rates previously available i
 n the general setting. The practical performance of our method is illustra
 ted through an academic example and applications to image processing.\n\nh
 ttps://indico.math.cnrs.fr/event/16777/
LOCATION:XR203 (XLIM)
URL:https://indico.math.cnrs.fr/event/16777/
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