Algorithms for the ferromagnetic Potts model on expanders

2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS)(2022)

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摘要
We give algorithms for approximating the partition function of the ferromagnetic Potts model on d-regular expanding graphs. We require much weaker expansion than in previous works; for example, the expansion exhibited by the hypercube suffices. The main improvements come from a significantly sharper analysis of standard polymer models, using extremal graph theory and applications of Karger’s algorithm to counting cuts that may be of independent interest. It is #BIS-hard to approximate the partition function at low temperatures on bounded-degree graphs, so our algorithm can be seen as evidence that hard instances of #BIS are rare. We believe that these methods can shed more light on other important problems such as sub-exponential algorithms for approximate counting problems.
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关键词
Potts model,random cluster model,approximate counting,approximate sampling,expander graphs
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