Sampling from the Hardcore Model on Random Regular Bipartite Graphs above the Uniqueness Threshold

Fuente: arXiv
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Hauptverfasser: Kocurek, Nicholas, Gharan, Shayan Oveis, Tjowasi, Dante
Format: Preprint
Veröffentlicht: 2026
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author Kocurek, Nicholas
Gharan, Shayan Oveis
Tjowasi, Dante
author_facet Kocurek, Nicholas
Gharan, Shayan Oveis
Tjowasi, Dante
contents We design an efficient sampling algorithm to generate samples from the hardcore model on random regular bipartite graphs as long as $λ\lesssim \frac{1}{\sqrtΔ}$, where $Δ$ is the degree. Combined with recent work of Jenssen, Keevash and Perkins this implies an FPRAS for the partition function of the hardcore model on random regular bipartite graphs at any fugacity. Our algorithm is shown by analyzing two new Markov chains that work in complementary regimes. Our proof then proceeds by showing the corresponding simplicial complexes are top-link spectral expanders and appealing to the trickle-down theorem to prove fast mixing.
format Preprint
id arxiv_https___arxiv_org_abs_2604_21847
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Sampling from the Hardcore Model on Random Regular Bipartite Graphs above the Uniqueness Threshold
Kocurek, Nicholas
Gharan, Shayan Oveis
Tjowasi, Dante
Data Structures and Algorithms
Computational Complexity
We design an efficient sampling algorithm to generate samples from the hardcore model on random regular bipartite graphs as long as $λ\lesssim \frac{1}{\sqrtΔ}$, where $Δ$ is the degree. Combined with recent work of Jenssen, Keevash and Perkins this implies an FPRAS for the partition function of the hardcore model on random regular bipartite graphs at any fugacity. Our algorithm is shown by analyzing two new Markov chains that work in complementary regimes. Our proof then proceeds by showing the corresponding simplicial complexes are top-link spectral expanders and appealing to the trickle-down theorem to prove fast mixing.
title Sampling from the Hardcore Model on Random Regular Bipartite Graphs above the Uniqueness Threshold
topic Data Structures and Algorithms
Computational Complexity
url https://arxiv.org/abs/2604.21847