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SUMMARY:Primal and dual variables decomposition methods in convex optimiza
 tion
DTSTART:20160415T101500
DTEND:20160415T111500
DTSTAMP:20260509T111222Z
UID:7c41f87fa48fa3b4750485150570e2d319c1ff2c122f4c59035e35d6
CATEGORIES:Conferences - Seminars
DESCRIPTION:Amir Beck\, Technion\nBio: Amir Beck is a Professor in the Dep
 artment of Industrial Engineering at The Technion—Israel Institute of Te
 chnology. He has published numerous papers\, has given invited lectures at
  international conferences\, and was awarded the Salomon Simon Mani Award 
 for Excellence in Teaching and the Henry Taub Research Prize. His research
  interests are in continuous optimization\, including theory\, algorithmic
  analysis\, and applications. He is an associate editor of Mathematics of 
 Operations Research\, the Journal of Optimization Theory and Applications\
 , Optimization Methods and Software and an area editor for optimization in
  Operations Research. His research has been supported by various funding a
 gencies\, including the Israel Science Foundation\, the German-Israeli Fou
 ndation\, the Binational US-Israel foundation\, the Israeli Science and En
 ergy Ministries and the European community.\nIn recent years\, due to the 
 “big data” revolution\, large-scale and even huge-scale applications h
 ave started to emerge\, and consequently the need for new\, fast and relia
 ble decomposition methods has become evident. We consider a class of block
  descent methods that involve the solution of many simple optimization sub
 problems of small dimension in place of the original problem. Rates of con
 vergence of different variants will be presented  and the dual functional
  form will be explored. The effectiveness of the methods will be illustrat
 ed on several scientific applications.
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STATUS:CONFIRMED
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