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X-WR-CALNAME;VALUE=TEXT:Eventi DIAG
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DTSTART:20251026T030000
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UID:calendar.30020.field_data.0@oba.diag.uniroma1.it
DTSTAMP:20260407T185709Z
CREATED:20251016T151054Z
DESCRIPTION:k-Median is a classic problem in data analysis where we aim to 
 minimize the sum of distances of points to a set of at most k centers. It 
 is NP-hard to solve accurately\, so most algorithms either resort to appro
 ximation\,or require assumptions. In this talk\, we present an algorithm t
 hat synthesizes both of these lines of research to obtain a (2 + ε) approx
 imation. The paper appeared at STOC 2025 is joint work with Vincent Cohen-
 Addad  (Google Research) Fabrizio Grandoni (IDSIA)\,  Euiwoong Lee (Univer
 sity of Michigan)\, Ola Svensson (University of Lausanne)
DTSTART;TZID=Europe/Paris:20251024T120000
DTEND;TZID=Europe/Paris:20251024T120000
LAST-MODIFIED:20251016T164044Z
LOCATION:DIAG - Aula Magna
SUMMARY:A (2 + ε)-Approximation Algorithm for Metric k-Median -  Chris Schw
 iegelshohn (Aarhus University)
URL;TYPE=URI:http://oba.diag.uniroma1.it/node/30020
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