BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Krylov Subspace Methods for Sparse Reconstruction - Silvia Gazzola
  (University of Bath)
DTSTART:20171102T090000Z
DTEND:20171102T095000Z
UID:TALK94348@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Krylov subspace methods are popular numerical linear algebra t
 ools that can be successfully employed to regularize linear large-scale in
 verse problems\, such as those arising in image deblurring and computed to
 mography. Though they are commonly used as purely iterative regularization
  methods (where the number of iterations acts as a regularization paramete
 r)\, they can be also employed in a hybrid fashion\, i.e.\, to solve Tikho
 nov regularized problems (where both the number of iterations and and the 
 Tikhonov parameter play the role of regularizations parameters\, which can
  be chosen adaptively). Krylov subspace methods can naturally handle uncon
 strained penalized least squares problems. The goal of this talk is to pre
 sent a common framework that exploits a flexible version of well-known Kry
 lov methods such as CGLS and GMRES to handle nonnegativity constraints and
  regularization terms expressed with respect to the 1-norm\, resulting in 
 an efficient way to enforce sparse reconstructions of the solution. Numeri
 cal experiments and comparisons with other well-known methods for the comp
 utation of nonnegative and sparse solutions will be presented. These resul
 ts have been obtained working jointly with James Nagy (Emory University)\,
  Paolo Novati (University of Trieste)\, Yves Wiaux (Heriot-Watt University
 )\, and Julianne Chung (Virginia Polytechnic Institute and State Universit
 y).
LOCATION:Seminar Room 1\, Newton Institute
END:VEVENT
END:VCALENDAR