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SUMMARY:#optazur Safe screening rules in convex optimisation: some example
 s in optimal design of experiments
DTSTART:20240603T131500Z
DTEND:20240603T141500Z
DTSTAMP:20260809T191400Z
UID:indico-event-10950@indico.math.cnrs.fr
DESCRIPTION:Speakers: Luc Pronzato\n\nMany convex optimisation problems ca
 n be formulated as the minimisation of a convex function of a probability 
 measure over a given set. Typical examples include determining the ellipso
 id of minimum volume\, or the smallest ball\, containing a set of points. 
 When it is known in advance that the optimal measure will be supported by 
 a small number of points\, it is advantageous to eliminate unnecessary poi
 nts (candidates) in order to simplify the problem. Safe screening rules ai
 m to eliminate such points: a rule defines a test to be applied to the can
 didates in order to eliminate those that are useless\; a rule is safe when
  no point supporting an optimal measure is eliminated. The aim is to apply
  the screening rule during optimisation\, regardless of the optimisation a
 lgorithm used. Usually\, the efficiency of elimination increases when appr
 oaching the optimum\, so the rule should be applied several (many) times a
 nd be as simple as possible. In addition to the construction of ellipsoids
  and balls of minimal volume\, I will present the construction of screenin
 g rules for different criteria in optimal design of experiments\, some of 
 them with a link to (quadratic) Lasso. Parts of this work are based on col
 laborations with Radoslav Harman (Comenius University\, Bratislava) and Gu
 illaume Sagnol (TU Berlin).\n\nhttps://indico.math.cnrs.fr/event/10950/
LOCATION:I3S
URL:https://indico.math.cnrs.fr/event/10950/
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