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arXiv 2603.02101math.CO
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在双部正则扩张图上采样反铁磁伊辛模型

Sampling from the antiferromagnetic Ising model on bipartite, regular expander graphs

Institute of Discrete Mathematics, Graz University of Technology(格拉茨工业大学离散数学研究所)

Anna Geisler, Mihyun Kang, Michail Sarantis, Ronen Wdowinski

AI总结:

本文研究了在双部正则扩张图上反铁磁伊辛模型的采样问题,发现Glauber动力学混合缓慢,但提出高效算法和配分函数近似方法。

摘要:

反铁磁伊辛模型通过权重指数衰减于诱导边数的子集采样图的顶点。我们研究了在双部正则图类上采样此模型的问题。我们证明了一个自然的采样器,即Glauber动力学,在广泛参数范围内混合速度呈指数衰减。另一方面,我们给出了一个高效的替代算法用于采样伊辛模型,并利用聚合模型和簇展开方法给出其配分函数的FPTAS。

英文摘要:

The antiferromagnetic Ising model samples subsets of vertices of a graph with weight decaying exponentially in the number of edges induced. We study the problem of sampling from this model on the class of bipartite, regular graphs with good vertex expansion. We show that a natural sampler, namely the Glauber dynamics, mixes exponentially slowly in a wide range of parameters. On the other hand, we give an efficient alternative algorithm for sampling from the Ising model and an FPTAS for its partition function, using polymer models and the cluster expansion method.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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