Journal of Statistical Physics

, Volume 174, Issue 2, pp 287–315 | Cite as

The Ising Partition Function: Zeros and Deterministic Approximation

  • Jingcheng Liu
  • Alistair SinclairEmail author
  • Piyush Srivastava


We study the problem of approximating the partition function of the ferromagnetic Ising model with both pairwise as well as higher order interactions (equivalently, in graphs as well as hypergraphs). Our approach is based on the classical Lee–Yang theory of phase transitions, along with a new Lee–Yang theorem for the Ising model with higher order interactions, and on an extension of ideas developed recently by Barvinok, and Patel and Regts that can be seen as an algorithmic realization of the Lee–Yang theory. Our first result is a deterministic polynomial time approximation scheme (an FPTAS) for the partition function in bounded degree graphs that is valid over the entire range of parameters \(\beta \) (the interaction) and \(\lambda \) (the external field), except for the case \(\left| \lambda \right| =1\) (the “zero-field” case). A polynomial time randomized approximation scheme (FPRAS) for all graphs and all \(\beta ,\lambda \), based on Markov chain Monte Carlo simulation, has long been known. Unlike most other deterministic approximation algorithms for problems in statistical physics and counting, our algorithm does not rely on the “decay of correlations” property, but, as pointed out above, on Lee–Yang theory. This approach extends to the more general setting of the Ising model on hypergraphs of bounded degree and edge size, where no previous algorithms (even randomized) were known for a wide range of parameters. In order to achieve this latter extension, we establish a tight version of the Lee–Yang theorem for the Ising model on hypergraphs, improving a classical result of Suzuki and Fisher.


Lee–Yang theorems Ising model Partition functions Higher-order interactions 



We thank Alexander Barvinok, Guus Regts and anonymous reviewers for helpful comments.


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Jingcheng Liu
    • 1
  • Alistair Sinclair
    • 1
    Email author
  • Piyush Srivastava
    • 2
  1. 1.Computer Science DivisionUC BerkeleyBerkeleyUSA
  2. 2.Tata Institute of Fundamental ResearchMumbaiIndia

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