# Regularized quasi-monotone method for stochastic optimization

arXiv: https://arxiv.org/abs/2107.03779 · HTML: https://mlsanity.com/paper/2107.03779/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2021.

## Built on by (most cited first, 1 of 1)

- [Training Structured Neural Networks Through Manifold Identification and Variance Reduction](https://mlsanity.com/paper/2112.02612/) (2021)

## Abstract (arXiv)

We adapt the quasi-monotone method from [2] for composite convex minimization in the stochastic setting. For the proposed numerical scheme we derive the optimal convergence rate in terms of the last iterate, rather than on average as it is standard for subgradient methods. The theoretical guarantee for individual convergence of the regularized quasi-monotone method is confirmed by numerical experiments on l1-regularized robust linear regression.