# Non-Smooth Setting of Stochastic Decentralized Convex Optimization Problem Over Time-Varying Graphs

arXiv: https://arxiv.org/abs/2307.00392 · HTML: https://mlsanity.com/paper/2307.00392/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2023.

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

- [Lower Bounds and Optimal Algorithms for Non-Smooth Convex Decentralized Optimization over Time-Varying Networks](https://mlsanity.com/paper/2405.18031/) (2024)

## Abstract (arXiv)

Distributed optimization has a rich history. It has demonstrated its effectiveness in many machine learning applications, etc. In this paper we study a subclass of distributed optimization, namely decentralized optimization in a non-smooth setting. Decentralized means that m agents (machines) working in parallel on one problem communicate only with the neighbors agents (machines), i.e. there is no (central) server through which agents communicate. And by non-smooth setting we mean that each agent has a convex stochastic non-smooth function, that is, agents can hold and communicate information only about the value of the objective function, which corresponds to a gradient-free oracle. In this paper, to minimize the global objective function, which consists of the sum of the functions of each agent, we create a gradient-free algorithm by applying a smoothing scheme via l2 randomization. We also verify in experiments the obtained theoretical convergence results of the gradient-free algorithm proposed in this paper.