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SUMMARY:Distributionally robust joint chance constraints with second-order
  moment information
DTSTART:20140516T101500
DTSTAMP:20260408T025935Z
UID:f50a21580b9eabeaf9d1bc1bd9799ca78de632677fdee00720faee4b
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Daniel Kuhn\, EPFL\nWe develop tractable semidefinite pr
 ogramming based approximations for distributionally robust individual and 
 joint chance constraints\, assuming that only the first- and second-order 
 moments as well as the support of the uncertain parameters are given. It i
 s known that robust chance constraints can be conservatively approximated 
 by Worst-Case Conditional Value-at-Risk (CVaR) constraints. We first prove
  that this approximation is exact for robust individual chance constraints
  with concave or (not necessarily concave) quadratic constraint functions\
 , and we demonstrate that the Worst-Case CVaR can be computed efficiently 
 for these classes of constraint functions. Next\, we study the Worst-Case 
 CVaR approximation for joint chance constraints. This approximation afford
 s intuitive dual interpretations and is provably tighter than two popular 
 benchmark approximations. The tightness depends on a set of scaling parame
 ters\, which can be tuned via a sequential convex optimization algorithm. 
 We show that the approximation becomes essentially exact when the scaling 
 parameters are chosen optimally and that the Worst-Case CVaR can be evalua
 ted efficiently if the scaling parameters are kept constant. We evaluate o
 ur joint chance constraint approximation in the context of a dynamic water
  reservoir control problem and numerically demonstrate its superiority ove
 r the two benchmark approximations.\nBio:Daniel Kuhn is Associate Professo
 r at the College of Management of Technology at EPFL where he holds the Ch
 air of Risk Analytics and Optimization (RAO). His current research interes
 ts are focused on the modeling of uncertainty\, the development of efficie
 nt computational methods for the solution of stochastic and robust optimiz
 ation problems and the design of approximation schemes that ensure their c
 omputational tractability. This work is primarily application-driven\, the
  main application areas being energy systems\, operations management and e
 ngineering.\nBefore joining EPFL\, Daniel Kuhn was a faculty member in the
  Department of Computing at Imperial College London (2007-2013) and a post
 doctoral research associate in the Department of Management Science and En
 gineering at Stanford University (2005-2006). He holds a PhD degree in Eco
 nomics from University of St. Gallen and an MSc degree in Theoretical Phys
 ics from ETH Zurich. He serves on the editorial boards of several academic
  journals including Energy systems\, Operations Research and Mathematical 
 Programming.
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STATUS:CONFIRMED
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