Generalizes exponential slow mixing of Glauber dynamics from Ising to multi-state Potts models and gives a polymer-model based deterministic approximation algorithm for the partition function on random regular bipartite graphs in the low-temperature non-uniqueness regime.
Approximate counting, uniform generation and rapidly mixing Markov chains
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4representative citing papers
Defines temporal conductance Φ for dynamic networks and proves the voter model consensus time is O(m/(d_min Φ)) with a tight lower bound.
Establishes a sharp computational phase transition for learning-to-sample from constantly bounded-width Ising models exactly at the spectral threshold λ_max(J)−λ_min(J)=1, with hardness above and tractability below under crypto assumptions.
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Counting and Sampling Anti-Ferromagnetic Potts Models on Random Regular Bipartite Graphs in the Non-uniqueness Regime
Generalizes exponential slow mixing of Glauber dynamics from Ising to multi-state Potts models and gives a polymer-model based deterministic approximation algorithm for the partition function on random regular bipartite graphs in the low-temperature non-uniqueness regime.
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Temporal Conductance and Bounds on the Voter Model for Dynamic Networks
Defines temporal conductance Φ for dynamic networks and proves the voter model consensus time is O(m/(d_min Φ)) with a tight lower bound.
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A computational phase transition for learning-to-sample from Ising models
Establishes a sharp computational phase transition for learning-to-sample from constantly bounded-width Ising models exactly at the spectral threshold λ_max(J)−λ_min(J)=1, with hardness above and tractability below under crypto assumptions.
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