# Accelerated iterative regularization via dual diagonal descent

arXiv: https://arxiv.org/abs/1912.12153 · HTML: https://mlsanity.com/paper/1912.12153/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Dec 2019.

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

- [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)
- [Theta-regularized Kriging: Modelling and Algorithms](https://mlsanity.com/paper/2604.14975/) (2026)

## Abstract (arXiv)

We propose and analyze an accelerated iterative dual diagonal descent algorithm for the solution of linear inverse problems with general regularization and data-fit functions. In particular, we develop an inertial approach of which we analyze both convergence and stability. Using tools from inexact proximal calculus, we prove early stopping results with optimal convergence rates for additive data-fit terms as well as more general cases, such as the Kullback-Leibler divergence, for which different type of proximal point approximations hold.