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SUMMARY:Distributed Optimization via the Alternating Direction Method of M
 ultipliers
DTSTART:20130712T101500
DTEND:20130712T113000
DTSTAMP:20260407T034453Z
UID:e06a888385176b414da6cb14529824469119396c46f4c2a772383159
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Stephen P. Boyd\, Stanford University\nAbstract :\nProbl
 ems in areas such as machine learning and dynamic optimization on a large 
 network lead to extremely large convex optimization problems\, with proble
 m data stored in a decentralized way\, and processing elements distributed
  across a network. We argue that the alternating direction method of multi
 pliers is well suited to such problems. The method was developed in the 19
 70s\, with roots in the 1950s\, and is equivalent or closely related to ma
 ny other algorithms\, such as dual decomposition\, the method of multiplie
 rs\, Douglas-Rachford splitting\, Spingarn's method of partial inverses\, 
 Dykstra's alternating projections\, Bregman iterative algorithms for l_1 p
 roblems\, proximal methods\, and others. After briefly surveying the theor
 y and history of the algorithm\, we discuss applications to statistical an
 d machine learning problems such as the lasso and support vector machines\
 , and to dynamic energy management problems arising in the smart grid.Bio 
 : Stephen P. Boyd is the Samsung Professor of Engineering\, and Professor 
 of Electrical Engineering in the Information Systems Laboratory at Stanfor
 d University. He has courtesy appointments in the Department of Management
  Science and Engineering and the Department of Computer Science\, and is m
 ember of the Institute for Computational and Mathematical Engineering. His
  current research focus is on convex optimization applications in control\
 , signal processing\, and circuit design.
LOCATION:CO3 (3rd floor entrance) http://plan.epfl.ch/?lang=fr&room=CO3
STATUS:CONFIRMED
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