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Trickle-down Theorems via C-Lorentzian Polynomials II: Pairwise Spectral Influence and Improved Dobrushin's Condition

Jonathan Leake, Shayan Oveis Gharan

TL;DR

The paper tackles rapid sampling from multi-state spin systems on d-partite simplicial complexes by introducing a pairwise spectral influence matrix ${\cal I}$ and establishing spectral-independence-based mixing guarantees under $\lambda_{\max}({\cal I})\le 1-\varepsilon$. It develops ${\cal C}$-Lorentzian polynomials and commutative $\pi$ maps derived from absorbing random walks to convert local spectral bounds at codimension-2 links into global eigenvalue controls, yielding improved trickle-down theorems for partite complexes. The main technical contribution shows that small $\lambda_{\max}({\cal I})$ implies explicit bounds on $\lambda_2(P_\tau)$ for all faces, which in turn gives rapid Glauber dynamics mixing and robust sampling guarantees beyond classical Dobrushin-type criteria. The framework unifies local-to-global spectral bounds with high-dimensional expander techniques, offering practical implications for sampling in statistical physics, graphical models, and related combinatorial constructions while suggesting directions for broader non-partite extensions and stronger mixing results.

Abstract

Let $μ$ be a probability distribution on a multi-state spin system on a set $V$ of sites. Equivalently, we can think of this as a $d$-partite simplical complex with distribution $μ$ on maximal faces. For any pair of vertices $u,v\in V$, define the pairwise spectral influence $\mathcal{I}_{u,v}$ as follows. Let $σ$ be a choice of spins $s_w\in S_w$ for every $w\in V \setminus \{u,v\}$, and construct a matrix in $\mathbb{R}^{(S_u\cup S_v)\times (S_u\cup S_v)}$ where for any $s_u\in S_u, s_v\in S_v$, the $(us_u,vs_v)$-entry is the probability that $s_v$ is the spin of $v$ conditioned on $s_u$ being the spin of $u$ and on $σ$. Then $\mathcal{I}_{u,v}$ is the maximal second eigenvalue of this matrix, over all choices of spins for all $w \in V \setminus \{u,v\}$. Equivalently, $\mathcal{I}_{u,v}$ is the maximum local spectral expansion of links of codimension $2$ that include a spin for every $w \in V \setminus \{u,v\}$. We show that if the largest eigenvalue of the pairwise spectral influence matrix with entries $\mathcal{I}_{u,v}$ is bounded away from 1, i.e. $λ_{\max}(\mathcal{I})\leq 1-ε$ (and $X$ is connected), then the Glauber dynamics mixes rapidly and generate samples from $μ$. This improves/generalizes the classical Dobrushin's influence matrix as the $\mathcal{I}_{u,v}$ lower-bounds the classical influence of $u\to v$. As a by-product, we also prove improved/almost optimal trickle-down theorems for partite simplicial complexes. The proof builds on the trickle-down theorems via $\mathcal{C}$-Lorentzian polynomials machinery recently developed by the authors and Lindberg.

Trickle-down Theorems via C-Lorentzian Polynomials II: Pairwise Spectral Influence and Improved Dobrushin's Condition

TL;DR

The paper tackles rapid sampling from multi-state spin systems on d-partite simplicial complexes by introducing a pairwise spectral influence matrix and establishing spectral-independence-based mixing guarantees under . It develops -Lorentzian polynomials and commutative maps derived from absorbing random walks to convert local spectral bounds at codimension-2 links into global eigenvalue controls, yielding improved trickle-down theorems for partite complexes. The main technical contribution shows that small implies explicit bounds on for all faces, which in turn gives rapid Glauber dynamics mixing and robust sampling guarantees beyond classical Dobrushin-type criteria. The framework unifies local-to-global spectral bounds with high-dimensional expander techniques, offering practical implications for sampling in statistical physics, graphical models, and related combinatorial constructions while suggesting directions for broader non-partite extensions and stronger mixing results.

Abstract

Let be a probability distribution on a multi-state spin system on a set of sites. Equivalently, we can think of this as a -partite simplical complex with distribution on maximal faces. For any pair of vertices , define the pairwise spectral influence as follows. Let be a choice of spins for every , and construct a matrix in where for any , the -entry is the probability that is the spin of conditioned on being the spin of and on . Then is the maximal second eigenvalue of this matrix, over all choices of spins for all . Equivalently, is the maximum local spectral expansion of links of codimension that include a spin for every . We show that if the largest eigenvalue of the pairwise spectral influence matrix with entries is bounded away from 1, i.e. (and is connected), then the Glauber dynamics mixes rapidly and generate samples from . This improves/generalizes the classical Dobrushin's influence matrix as the lower-bounds the classical influence of . As a by-product, we also prove improved/almost optimal trickle-down theorems for partite simplicial complexes. The proof builds on the trickle-down theorems via -Lorentzian polynomials machinery recently developed by the authors and Lindberg.

Paper Structure

This paper contains 32 sections, 30 theorems, 108 equations.

Key Result

Theorem 1.1

If $\rho(I)\leq 1-\epsilon$, then $\mu$ is $2/\epsilon$-spectrally independent and the Glauber dynamics mixes in $O(d\log d/\epsilon)$ steps, where $\rho(I)$ is the spectral radius of $I$ (that is, the largest eigenvalue of $I$ in absolute value).

Theorems & Definitions (55)

  • Theorem 1.1: see e.g., Hay06DGJ09BCCPSV06Liu21
  • Lemma 1.2
  • Theorem 1.3: Main
  • Theorem 1.4: Main technical
  • Corollary 1.5
  • Lemma 1.6
  • Theorem 1.7: Zuk03BHV07Opp18
  • Definition 2.1: ${\cal C}$-Lorentzian polynomials; Defn. 2.1 of BL23
  • Lemma 2.2
  • proof
  • ...and 45 more