Journal article
Fast algorithms for general spin systems on bipartite expanders
- Abstract:
- A spin system is a framework in which the vertices of a graph are assigned spins from a finite set. The interactions between neighbouring spins give rise to weights, so a spin assignment can also be viewed as a weighted graph homomorphism. The problem of approximating the partition function (the aggregate weight of spin assignments) or of sampling from the resulting probability distribution is typically intractable for general graphs. In this work, we consider arbitrary spin systems on bipartite expander ∆-regular graphs, including the canonical class of bipartite random ∆-regular graphs. We develop fast approximate sampling and counting algorithms for general spin systems whenever the degree and the spectral gap of the graph are sufficiently large. Roughly, this guarantees that the spin system is in the so-called low-temperature regime. Our approach generalises the techniques of Jenssen et al. and Chen et al. by showing that typical configurations on bipartite expanders correspond to “bicliques” of the spin system; then, using suitable polymer models, we show how to sample such configurations and approximate the partition function in O˜(n 2 ) time, where n is the size of the graph.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Access Document
- Files:
-
-
(Preview, Accepted manuscript, 449.8KB, Terms of use)
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- Publisher copy:
- 10.1145/3470865
Authors
- Publisher:
- Association for Computing Machinery
- Journal:
- ACM Transactions on Computation Theory More from this journal
- Volume:
- 13
- Issue:
- 4
- Article number:
- 25
- Publication date:
- 2021-08-31
- Acceptance date:
- 2021-04-28
- DOI:
- EISSN:
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1942-3462
- ISSN:
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1942-3454
- Language:
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English
- Keywords:
- Pubs id:
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1174054
- Local pid:
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pubs:1174054
- Deposit date:
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2021-05-04
Terms of use
- Copyright holder:
- Association for Computing Machinery.
- Copyright date:
- 2021
- Rights statement:
- © 2021 Association for Computing Machinery.
- Notes:
- This is the accepted manuscript version of the article. The final version is available online from ACM Digital Library at: https://doi.org/10.1145/3470865
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