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SUMMARY:Optimization of the sum of a convex surrogate and quadratic object
 ive
DTSTART:20160113T110000
DTEND:20160113T120000
DTSTAMP:20260916T043958Z
UID:d98a3618613a646d3485449a21b744980c15de273dde3792572f5062
CATEGORIES:Conferences - Seminars
DESCRIPTION:Olivier Huber - University of Wisconsin-Madison\nWe consider a
  convex optimization problem where the objective function is the sum f(x) 
 + g(y) and the coupling between the variables x and y is at the constraint
  level. We focus on the case where g is not available in closed form and c
 an only be evaluated at a given point by running a long simulation process
 . The results of interest are prices formed from the gradient of g. It is 
 assumed that the function g is convex or can be (reasonably) approximated 
 by a convex one. We choose to use an approximation of g defined as a point
 wise supremum over a family of piecewise affine functions. This part of th
 e procedure is carried out offline\, and uses evaluations of g to define t
 he approximation from its epigraph. We report on using the Moreau-Yosida r
 egularization on our approximation function to return a smoothed value of 
 the gradient that reduces the volatility in the prices. We outline some re
 sults in the context of a reserve energy market planning problem.
LOCATION:Room GC B2 424 http://map.epfl.ch/?q=gcb2424
STATUS:CONFIRMED
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