Iterative regularization via a dual diagonal descent method


In the context of linear inverse problems, we propose and study general iterative regularization method allowing to consider classes of regularizers and data-fit terms.

The algorithm we propose is based on a primal-dual diagonal descent method, designed to solve hierarchical optimization problems.

Our analysis establishes convergence as well as stability results, in presence of error in the data.

In this noisy case, the number of iterations is shown to act as a regularization parameter, which makes our algorithm an iterative regularization method.

Date: Dec 14, 2016 at 16:30 h
Venue: Beauchef 851, Torre Norte Piso 7 Sala de Seminarios John Von Neumann.
Speaker: Guillaume Garrigos
Affiliation: Istituto Italiano de Tecnología.
Coordinator: Prof. Abderrahim Hantoute

Posted on Dec 13, 2016 in Optimization and Equilibrium, Seminars