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Improved Mixing of Critical Hardcore Model

Zongchen Chen, Tianhui Jiang

TL;DR

This work addresses sampling from the hardcore model on graphs with maximum degree $\Delta\ge3$ at the critical fugacity $\lambda_c(\Delta)$. It develops an optimal $\ell_\infty$-spectral-independence bound in the subcritical regime, and uses spectral-independence localization to derive a polynomial-time mixing bound for Glauber dynamics at criticality, achieving $T_{\mathrm{mix}}=\tilde{O}(n^{4+O(1/\Delta)})$. The main technical contribution is the explicit upper bound $C^\infty_{SI}=(1+\hat{x})/(1-d\hat{x})$ for the subcritical hardcore model, with $\hat{x}$ the fixed point of $F_{d,\lambda}$, plus a matching lower bound on finite trees that establishes tightness. This yields a substantial improvement over prior bounds (e.g., $\tilde{O}(n^{12.88+O(1/\Delta)})$) and demonstrates the power of the spectral-independence approach in handling critical-phase sampling problems.

Abstract

The hardcore model is one of the most classic and widely studied examples of undirected graphical models. Given a graph $G$, the hardcore model describes a Gibbs distribution of $λ$-weighted independent sets of $G$. In the last two decades, a beautiful computational phase transition has been established at a precise threshold $λ_c(Δ)$ where $Δ$ denotes the maximum degree, where the task of sampling independent sets transitions from polynomial-time solvable to computationally intractable. We study the critical hardcore model where $λ= λ_c(Δ)$ and show that the Glauber dynamics, a simple yet popular Markov chain algorithm, mixes in $\tilde{O}(n^{4+O(1/Δ)})$ time on any $n$-vertex graph of maximum degree $Δ\geq3$, significantly improving the previous upper bound $\tilde{O}(n^{12.88+O(1/Δ)})$ by the recent work arXiv:2411.03413. Our improvement comes from an optimal bound on the $\ell_\infty$-spectral independence for the hardcore model at all subcritical fugacity $λ< λ_c(Δ)$.

Improved Mixing of Critical Hardcore Model

TL;DR

This work addresses sampling from the hardcore model on graphs with maximum degree at the critical fugacity . It develops an optimal -spectral-independence bound in the subcritical regime, and uses spectral-independence localization to derive a polynomial-time mixing bound for Glauber dynamics at criticality, achieving . The main technical contribution is the explicit upper bound for the subcritical hardcore model, with the fixed point of , plus a matching lower bound on finite trees that establishes tightness. This yields a substantial improvement over prior bounds (e.g., ) and demonstrates the power of the spectral-independence approach in handling critical-phase sampling problems.

Abstract

The hardcore model is one of the most classic and widely studied examples of undirected graphical models. Given a graph , the hardcore model describes a Gibbs distribution of -weighted independent sets of . In the last two decades, a beautiful computational phase transition has been established at a precise threshold where denotes the maximum degree, where the task of sampling independent sets transitions from polynomial-time solvable to computationally intractable. We study the critical hardcore model where and show that the Glauber dynamics, a simple yet popular Markov chain algorithm, mixes in time on any -vertex graph of maximum degree , significantly improving the previous upper bound by the recent work arXiv:2411.03413. Our improvement comes from an optimal bound on the -spectral independence for the hardcore model at all subcritical fugacity .
Paper Structure (9 sections, 9 theorems, 64 equations)

This paper contains 9 sections, 9 theorems, 64 equations.

Key Result

Theorem 1.1

For any $n$-vertex graph $G=(V,E)$ of maximum degree $\Delta \ge 3$, the Glauber dynamics for the hardcore model on $G$ with fugacity $\lambda = \lambda_c(\Delta)$ satisfies

Theorems & Definitions (20)

  • Theorem 1.1
  • Remark 1.2
  • Definition 2.1: Influence, anari2020spectral
  • Definition 2.2: Spectral independence, anari2020spectral
  • Proposition 2.3: Spectral independence implies rapid mixing, CCYZ24chen2022localization
  • Proposition 2.4: chen2023rapid
  • Theorem 3.1: Upper bound
  • proof : Proof of \ref{['thm:main']}
  • Theorem 3.2: Lower bound
  • Lemma 3.3
  • ...and 10 more