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Cited by 360

The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.

Worm Algorithms for Classical Statistical Models

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Prokof'ev, Nikolay, Svistunov, Boris·2001·Physical Review Letters

Abstract

We show that high-temperature expansions provide a basis for the novel approach to efficient Monte Carlo simulations. "Worm" algorithms utilize the idea of updating closed-path configurations (produced by high-temperature expansions) through the motion of end points of a disconnected path. An amazing result is that local, Metropolis-type schemes using this approach appear to have dynamical critical exponents close to zero (i.e., their efficiency is comparable to the best cluster methods) as proved by finite-size scaling of the autocorrelation time for various universality classes.

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Metadata

Authors
Prokof'ev, N., & Svistunov, B.
Venue
Physical Review Letters
Published
2001-09-27
Volume
87
Issue
16
Pages
160601-160601
ISSN
  • 0031-9007 (ISSN-L)
  • 1079-7114
OpenAlex
W1981361326
Cited by
360
References
15
FWCI
3.27
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Worm Algorithms for Classical Statistical Models. https://app.cochat.ai/papers/10.1103/physrevlett.87.160601
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Prokof'ev, N., & Svistunov, B. (2001). Worm Algorithms for Classical Statistical Models. Physical Review Letters. https://doi.org/10.1103/physrevlett.87.160601

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.