# Accelerated Distributed Dual Averaging over Evolving Networks of Growing Connectivity

## TL;DR

This paper investigates how evolving network topologies with increasing connectivity can accelerate distributed dual averaging algorithms, providing design strategies and theoretical analysis for improved convergence in multi-agent optimization.

## Contribution

It introduces a method for designing network topologies through edge selection and scheduling to optimize DDA convergence, filling a gap in understanding the impact of network design.

## Key findings

- Network connectivity growth improves DDA convergence rate.
- Optimal edge selection balances connectivity and resource use.
- Numerical results confirm theoretical acceleration predictions.

## Abstract

We consider the problem of accelerating distributed optimization in multi-agent networks by sequentially adding edges. Specifically, we extend the distributed dual averaging (DDA) subgradient algorithm to evolving networks of growing connectivity and analyze the corresponding improvement in convergence rate. It is known that the convergence rate of DDA is influenced by the algebraic connectivity of the underlying network, where better connectivity leads to faster convergence. However, the impact of network topology design on the convergence rate of DDA has not been fully understood. In this paper, we begin by designing network topologies via edge selection and scheduling. For edge selection, we determine the best set of candidate edges that achieves the optimal tradeoff between the growth of network connectivity and the usage of network resources. The dynamics of network evolution is then incurred by edge scheduling. Further, we provide a tractable approach to analyze the improvement in the convergence rate of DDA induced by the growth of network connectivity. Our analysis reveals the connection between network topology design and the convergence rate of DDA, and provides quantitative evaluation of DDA acceleration for distributed optimization that is absent in the existing analysis. Lastly, numerical experiments show that DDA can be significantly accelerated using a sequence of well-designed networks, and our theoretical predictions are well matched to its empirical convergence behavior.