# Distributed Aggregative Optimization over Multi-Agent Networks

arXiv: https://arxiv.org/abs/2005.13436 · HTML: https://mlsanity.com/paper/2005.13436/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2020.

## Built on by (most cited first, 3 of 3)

- [On Faster Convergence of Scaled Sign Gradient Descent](https://mlsanity.com/paper/2109.01806/) (2021)
- [Communication-Efficient {Federated} Learning Using Censored Heavy Ball Descent](https://mlsanity.com/paper/2209.11944/) (2022)
- [Accelerated Distributed Aggregative Optimization](https://mlsanity.com/paper/2304.08051/) (2023)

## Abstract (arXiv)

This paper proposes a new framework for distributed optimization, called distributed aggregative optimization, which allows local objective functions to be dependent not only on their own decision variables, but also on the average of summable functions of decision variables of all other agents. To handle this problem, a distributed algorithm, called distributed gradient tracking (DGT), is proposed and analyzed, where the global objective function is strongly convex, and the communication graph is balanced and strongly connected. It is shown that the algorithm can converge to the optimal variable at a linear rate. A numerical example is provided to corroborate the theoretical result.