BEGIN:VCALENDAR VERSION:2.0 PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4// BEGIN:VEVENT UID:20250712T101632EDT-5939AFT1sM@132.216.98.100 DTSTAMP:20250712T141632Z DESCRIPTION:Nonsmooth\, Nonconvex Optimization: Algorithms and Examples\n Ab stract:\n In many applications one wishes to minimize an objective function that is not convex and is not differentiable at its minimizers. We discus s two algorithms for minimization of nonsmooth\, nonconvex functions. Grad ient Sampling is a simple method that\, although computationally intensive \, has a nice convergence theory. The method is robust and the convergence theory has recently been extended to constrained problems. BFGS is a well known method\, developed for smooth problems\, but which is remarkably ef fective for nonsmooth problems too. Although our theoretical results in th e nonsmooth case are quite limited\, we have made some remarkable empirica l observations and have had broad success in applications.  Limited Memory BFGS is a popular extension for large problems\, and it is also applicabl e to the nonsmooth case\, although our experience with it is more mixed. T hroughout the talk we illustrate the ideas through examples\, some very ea sy and some very challenging. Our work is with Jim Burke (U. Washington) a nd Adrian Lewis (Cornell).\n  \n DTSTART:20161024T190000Z DTEND:20161024T200000Z LOCATION:Room 920\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue Sherbrooke Ouest SUMMARY:Michael L. Overton Courant (Institute of Mathematical Sciences\, Ne w York University) URL:/mathstat/channels/event/michael-l-overton-courant -institute-mathematical-sciences-new-york-university-263436 END:VEVENT END:VCALENDAR