Abstract
The smallness parameter determining the possibility of application of the n-vicinity method for calculating the free energy of a spin system has been found. The types of spin systems for which approximation of the n-vicinity method is valid have been determined. It is shown that this method is adequate for studying the properties of spin systems, in which the effective number of nearest neighbors is more than \(\frac{{16}}{3}\).
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REFERENCES
D. Amit, H. Gutfreund, and H. Sompolinsky, Phys. Rev. Lett. 55, 1530 (1985).
D. Amit, H. Gutfreund, and H. Sompolinsky, Ann. Phys. 173, 30 (1987).
A. A. Frolov, D. Husek, and I. P. Muraviev, Opt. Mem. Neural Networks 12, 177 (2004).
I. Karandashev, B. Kryzhanovsky, and L. Litinskii, Phys. Rev. E 85, 041925 (2012).
Y. LeCun, Y. Bengio, and G. Hinton, Nature 521, 436 (2015).
H. W. Lin and M. Tegmark, J. Stat. Phys. 168, 1223 (2017).
R. J. Baxter, Exactly Solved Models in Statistical Mechanics (Acad. Press, London, 1982).
B. V. Kryzhanovsky and L. B. Litinskii, Dokl. Akad. Nauk 459 (6), 680 (2014).
P. D. Beale, Phys. Rev. Lett. 76, 78 (1996).
B. V. Kryzhanovsky, M. Yu. Malsagov, and I. M. Karandashev, Entropy 20 (9), 585 (2018).
L. Litinskii and B. Kryzhanovsky, Physica A 510, 702 (2018).
J. M. Dixon, J. A. Tuszynski, and E. J. Carpenter, Physica A 349, 487 (2005).
R. Häggkvist, A. Rosengren, D. Andrén, et al., Phys. Rev. E 69 (4), (2004).
B. Kryzhanovsky and M. Malsagov, Opt. Mem. Neural Networks 25 (1), 1 (2016).
Y. M. Karandashev and M. Yu. Malsagov, Opt. Mem. Neural Networks 26 (2), 87 (2017).
ACKNOWLEDGMENTS
We are grateful to Ya.M. Karandashev for help in carrying out numerical experiments.
Funding
This study was supported by the Russian Foundation for Basic Research, project no. 18-07-00750.
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Translated by A. Sin’kov
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Kryzhanovsky, B.V. Approximation of the n-Vicinity Method. Dokl. Phys. 64, 280–284 (2019). https://doi.org/10.1134/S1028335819070103
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DOI: https://doi.org/10.1134/S1028335819070103