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UID:20251122T001828EST-5621m7Au7L@132.216.98.100
DTSTAMP:20251122T051828Z
DESCRIPTION:Title: An Inexact Trust-Region Algorithm for Nonsmooth Nonconve
 x Regularized Problems.\n\nAbstract: Many inverse problems require minimiz
 ing the sum of smooth and nonsmooth functions. For example\, basis pursuit
  denoise applications in data science require minimizing a measure of data
  misfit plus an L1-regularizer. Similar problems arise in the optimal cont
 rol of partial differential equations (PDEs) when sparsity of the control 
 is desired. For such applications\, it is often impossible to compute exac
 t derivatives or function values due to problem size and complexity. We de
 velop a novel inexact trust-region method to minimize the sum of a smooth 
 nonconvex function and a nonsmooth convex function. The trust-region routi
 ne permits and systematically controls the use of inexact objective functi
 on and derivative evaluations. When using a quadratic Taylor model for the
  trust-region subproblem\, our algorithm is an inexact\, matrix-free proxi
 mal Newton-type method that permits indefinite Hessians. Using unconstrain
 ed and convex constrained trust-region methods as motivation\, we describe
  various methods for efficiently solving the nonsmooth trust-region subpro
 blem. We also prove global convergence of our method in Hilbert space and 
 demonstrate its efficacy on three examples from data science and PDE-const
 rained optimization.\n\nWeb site : https://dms.umontreal.ca/~mathapp/\n\n 
 \n
DTSTART:20221024T200000Z
DTEND:20221024T210000Z
LOCATION:Room 1104\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Robert Baraldi  (Sandia National Labs)
URL:/mathstat/channels/event/robert-baraldi-sandia-nat
 ional-labs-342834
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