par Rebegoldi, Simone;Bonettini, Silvia;Loris, Ignace ;Porta, Federica;Prato, Marco
Référence SIAM Conference on Imaging Science (23-26/05/2016: Albuquerque, New Mexico, USA)
Publication Non publié, 2016-05-23
Communication à un colloque
Résumé : We present a variable metric line--search based proximal--gradient method for the minimization of the sum of a smooth, possibly nonconvex function plus a convex, possibly nonsmooth term. The strong convergence of the method can be proved if the objective function satisfies the Kurdyka-Lojasiewicz property at each point of its domain. Numerical experience on some nonconvex image reconstruction problems shows the proposed approach is competitive with other state-of-the-art methods.