# Don't relax: early stopping for convex regularization

arXiv: https://arxiv.org/abs/1707.05422 · HTML: https://mlsanity.com/paper/1707.05422/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2017.

## Built on by (most cited first, 4 of 4)

- [From inexact optimization to learning via gradient concentration](https://mlsanity.com/paper/2106.05397/) (2021)
- [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)
- [Iterative Regularization with k-support Norm: An Important Complement to Sparse Recovery](https://mlsanity.com/paper/2401.05394/) (2023)

## Abstract (arXiv)

We consider the problem of designing efficient regularization algorithms when regularization is encoded by a (strongly) convex functional. Unlike classical penalization methods based on a relaxation approach, we propose an iterative method where regularization is achieved via early stopping. Our results show that the proposed procedure achieves the same recovery accuracy as penalization methods, while naturally integrating computational considerations. An empirical analysis on a number of problems provides promising results with respect to the state of the art.