The paper studies how to establish spectral independence, a key concept in sampling, without relying on total influence bounds. The authors apply an approximate inverse of the influence matrix to obtain constant upper bounds on spectral independence for two foundational Gibbs distributions known to have unbounded total influences:
The monomer-dimer model on graphs with large girth (including trees). Prior to this work, such results were only known for graphs with constant maximum degrees or infinite regular trees.
The hardcore model on trees with fugacity λ < e^2. This significantly improves upon the current threshold λ < 1.3, established in prior work.
The authors introduce a new direct approach for establishing spectral independence, based on an approximate inverse of the influence matrix. This method is particularly intuitive on trees while exhibiting promising potential for generalization to non-trees.
For the monomer-dimer model, the authors prove that on any tree, the spectral independence is bounded by a constant, confirming a conjecture. They also establish a general trade-off between the girth and the spectral independence for the monomer-dimer model on general graphs.
For the hardcore model on trees, the authors push the threshold of λ for spectral independence to λ < e^2, significantly improving upon the previous threshold of λ < 1.3. This result suggests that either fast mixing holds beyond the reconstruction threshold, or the reconstruction threshold is actually higher.
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