Nonlinear Exchange Dynamics for Independent Sets

2026-07-31

AI Summary

This work addresses the lack of a theoretical framework for analyzing nonlinear dynamics in hard-core models, which hinders efficient sampling of independent sets with prescribed densities or marginal distributions. Focusing on two classes of nonlinear dynamics—mean-field dynamics preserving global density and local dynamics preserving single-site occupation probabilities—the paper establishes, for the first time, rigorous convergence guarantees and entropy decay properties. Through coupling arguments, relative entropy analysis, and a Kac-inspired linear Markov chain approximation, it demonstrates that both dynamics exhibit nearly linear convergence rates at low densities, with mean-field dynamics further displaying exponential relative entropy decay up to a critical density. Building on these insights, the authors propose a novel particle-based sampling algorithm that achieves comparable computational complexity while offering greater practical implementability.

Abstract

In recent years, nonlinear dynamics derived from kinetic theory have gained attention in the context of sampling configurations of spin systems such as the Ising model. We focus on nonlinear dynamics for the hard-core model, a canonical spin system with hard constraints that specifies a distribution over independent sets in a graph, weighted by their sizes. We explore two distinct types of nonlinear dynamics: the mean-field dynamics, which preserves the density (or average size) of independent sets, and the single-site dynamics, which preserves the marginal vector (i.e., the occupancy probabilities of the vertices). These dynamics are natural stochastic processes for sampling from the hard-core model with a specified density or marginal vector, respectively, both of which are canonical instances of maximum entropy distributions that have been studied in various contexts. In contrast to linear Markov chains, there is a significant lack of a fundamental theoretical framework for nonlinear dynamics. We develop foundational theoretical tools for analyzing nonlinear dynamics within the context of the hard-core model. We establish almost linear convergence of both the mean-field and single-site dynamics at sufficiently low density through novel coupling arguments. We also establish exponential decay of relative entropy for the mean-field dynamics all the way up to the critical density. Additionally, we design new algorithms for sampling from the hard-core distribution with either a specified density or a specified marginal vector. These algorithms are based on a related linear Markov chain, called the particle-system dynamics and inspired by the so-called Kac's program, that approximates the associated nonlinear dynamics. As we demonstrate in the paper, they are comparable in time complexity, but simpler to implement, than traditional approaches based on learning parameter values.

Problem

• nonlinear dynamics
• hard-core model
• independent sets
• sampling
• convergence

Innovation

• nonlinear dynamics
• hard-core model
• mean-field dynamics
• single-site dynamics
• entropy decay