# Centralized and distributed online learning for sparse time-varying optimization

arXiv: https://arxiv.org/abs/2001.11939 · HTML: https://mlsanity.com/paper/2001.11939/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2020.

## Built on by (most cited first, 5 of 5)

- [Byzantine-Robust Distributed Online Learning: Taming Adversarial Participants in An Adversarial Environment](https://mlsanity.com/paper/2307.07980/) (2023)
- [Dynamic Regret of Online Mirror Descent for Relatively Smooth Convex Cost Functions](https://mlsanity.com/paper/2202.12843/) (2022)
- [Locally Differentially Private Distributed Online Learning with Guaranteed Optimality](https://mlsanity.com/paper/2306.14094/) (2023)
- [Follow The Approximate Sparse Leader for No-Regret Online Sparse Linear Approximation](https://mlsanity.com/paper/2501.00799/) (2025)
- [Fast sparse optimization via adaptive shrinkage](https://mlsanity.com/paper/2501.12236/) (2025)

## Abstract (arXiv)

The development of online algorithms to track time-varying systems has drawn a lot of attention in the last years, in particular in the framework of online convex optimization. Meanwhile, sparse time-varying optimization has emerged as a powerful tool to deal with widespread applications, ranging from dynamic compressed sensing to parsimonious system identification. In most of the literature on sparse time-varying problems, some prior information on the system's evolution is assumed to be available. In contrast, in this paper, we propose an online learning approach, which does not employ a given model and is suitable for adversarial frameworks. Specifically, we develop centralized and distributed algorithms, and we theoretically analyze them in terms of dynamic regret, in an online learning perspective. Further, we propose numerical experiments that illustrate their practical effectiveness.