# Particle filter efficiency under limited communication

## TL;DR

This paper investigates how limited communication structures in particle filters affect their convergence and stability, demonstrating that randomized local communication can maintain efficiency and Monte Carlo convergence rates.

## Contribution

It introduces the analysis of communication structure effects on particle filter stability and proposes randomized local communication to improve distributed algorithm efficiency.

## Key findings

- Limited communication affects convergence stability.
- Randomized local communication maintains Monte Carlo rate.
- Good mixing properties ensure algorithm stability.

## Abstract

Sequential Monte Carlo methods are typically not straightforward to implement on parallel architectures. This is because standard resampling schemes involve communication between all particles. The $\alpha$-sequential Monte Carlo method was proposed recently as a potential solution to this which limits communication between particles. This limited communication is controlled through a sequence of stochastic matrices known as $\alpha$-matrices. We study the influence of the communication structure on the convergence and stability properties of the resulting algorithms. In particular, we quantitatively show that the mixing properties of the $\alpha$-matrices play an important role in the stability properties of the algorithm. Moreover, we prove that one can ensure good mixing properties by using randomized communication structures where each particle only communicates with a few neighboring particles. The resulting algorithms converge at the usual Monte Carlo rate. This leads to efficient versions of distributed sequential Monte Carlo.