# Iterative regularization via dual diagonal descent

arXiv: https://arxiv.org/abs/1610.02170 · HTML: https://mlsanity.com/paper/1610.02170/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2016.

## Built on by (most cited first, 3 of 3)

- [Convex Bi-Level Optimization Problems with Non-smooth Outer Objective Function](https://mlsanity.com/paper/2307.08245/) (2023)
- [Iterative regularization for low complexity regularizers](https://mlsanity.com/paper/2202.00420/) (2022)
- [Iterative regularization in classification via hinge loss diagonal descent](https://mlsanity.com/paper/2212.12675/) (2022)

## Abstract (arXiv)

In the context of linear inverse problems, we propose and study a general iterative regularization method allowing to consider large classes of regularizers and data-fit terms. The algorithm we propose is based on a primal-dual diagonal {descent} method. Our analysis establishes convergence as well as stability results. Theoretical findings are complemented with numerical experiments showing state of the art performances.