Abstract:
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.