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UID:20250813T155746EDT-3577JBUnLp@132.216.98.100
DTSTAMP:20250813T195746Z
DESCRIPTION:Title: Hidden convexity in nonconvex optimization.\n\nAbstract:
  In nonconvex optimization\, not only the objective but even the feasible 
 set may lack convexity. It may seem therefore that the concepts and method
 ology of convex optimization can no longer have a fundamental role\, but t
 his is actually wrong. Standard sufficient conditions for local optimality
  in nonlinear programming and its extensions turn out to correspond to cha
 racterizing optimality in terms of a local convex-concave-type saddle poin
 t of an augmented Lagrangian function. Algorithms that effectively in both
  primal and dual elements are thereby revealed as working just as they wou
 ld in the convex case.\n\n \n\n \n\nTo register contact : appliedseminars 
 [at] math.mcgill.ca\n
DTSTART:20211018T200000Z
DTEND:20211018T210000Z
SUMMARY:Terry Rockafellar (University of Washington)
URL:/mathstat/channels/event/terry-rockafellar-univers
 ity-washington-333856
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