# Distributed Zero-Order Algorithms for Nonconvex Multi-Agent Optimization

arXiv: https://arxiv.org/abs/1908.11444 · HTML: https://mlsanity.com/paper/1908.11444/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2019.

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

- [Fine-Tuning Language Models with Just Forward Passes](https://mlsanity.com/paper/2305.17333/) (2023)
- [Distributed Multi-Agent Reinforcement Learning Based on Graph-Induced Local Value Functions](https://mlsanity.com/paper/2202.13046/) (2022)
- [Asynchronous Distributed Reinforcement Learning for LQR Control via Zeroth-Order Block Coordinate Descent](https://mlsanity.com/paper/2107.12416/) (2021)
- [Convergence Analysis of Nonconvex Distributed Stochastic Zeroth-order Coordinate Method](https://mlsanity.com/paper/2103.12954/) (2021)

## Abstract (arXiv)

Distributed multi-agent optimization finds many applications in distributed learning, control, estimation, etc. Most existing algorithms assume knowledge of first-order information of the objective and have been analyzed for convex problems. However, there are situations where the objective is nonconvex, and one can only evaluate the function values at finitely many points. In this paper we consider derivative-free distributed algorithms for nonconvex multi-agent optimization, based on recent progress in zero-order optimization. We develop two algorithms for different settings, provide detailed analysis of their convergence behavior, and compare them with existing centralized zero-order algorithms and gradient-based distributed algorithms.