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math.OC2020
Using gradient directions to get global convergence of Newton-type methods
Daniela di Serafino, Gerardo Toraldo, Marco Viola
The renewed interest in Steepest Descent (SD) methods following the work of Barzilai and Borwein [IMA Journal of Numerical Analysis, 8 (1988)] has driven us to consider a globaliza…
math.OC2019
A subspace-accelerated split Bregman method for sparse data recovery with joint l1-type regularizers
Valentina De Simone, Daniela di Serafino, Marco Viola
We propose a subspace-accelerated Bregman method for the linearly constrained minimization of functions of the form ,…
math.OC2018
ACQUIRE: an inexact iteratively reweighted norm approach for TV-based Poisson image restoration
Daniela di Serafino, Germana Landi, Marco Viola
We propose a method, called ACQUIRE, for the solution of constrained optimization problems modeling the restoration of images corrupted by Poisson noise. The objective function is…