# Distributed Optimization for Second-Order Multi-Agent Systems with Dynamic Event-Triggered Communication

arXiv: https://arxiv.org/abs/1803.06380 · HTML: https://mlsanity.com/paper/1803.06380/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2018.

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

- [Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits](https://mlsanity.com/paper/2110.01463/) (2021)
- [Convergence Analysis of Nonconvex Distributed Stochastic Zeroth-order Coordinate Method](https://mlsanity.com/paper/2103.12954/) (2021)
- [A Stochastic Approximation Approach for Efficient Decentralized Optimization on Random Networks](https://mlsanity.com/paper/2410.18774/) (2024)

## Abstract (arXiv)

In this paper, we propose a fully distributed algorithm for second-order continuous-time multi-agent systems to solve the distributed optimization problem. The global objective function is a sum of private cost functions associated with the individual agents and the interaction between agents is described by a weighted undirected graph. We show the exponential convergence of the proposed algorithm if the underlying graph is connected, each private cost function is locally gradient-Lipschitz-continuous, and the global objective function is restricted strongly convex with respect to the global minimizer. Moreover, to reduce the overall need of communication, we then propose a dynamic event-triggered communication mechanism that is free of Zeno behavior. It is shown that the exponential convergence is achieved if the private cost functions are also globally gradient-Lipschitz-continuous. Numerical simulations are provided to illustrate the effectiveness of the theoretical results.