Abstract
In the literature the most frequently cited data are quite contradictory, and there is no consensus on the global minimum value of 2D Edwards-Anderson (2D EA) Ising model. By means of computer simulations, with the help of exact polynomial Schraudolph-Kamenetsky algorithm, we examined the global minimum depth in 2D EA-type models. We found a dependence of the global minimum depth on the dimension of the problem N and obtained its asymptotic value in the limit N → ∞. We believe these evaluations can be further used for examining the behavior of 2D Bayesian models often used in machine learning and image processing.
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The work was supported by Russian Foundation for Basic Research (RFBR Project 18-07-00750).
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Karandashev, I., Kryzhanovsky, B. (2019). Global Minimum Depth in Edwards-Anderson Model. In: Macintyre, J., Iliadis, L., Maglogiannis, I., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2019. Communications in Computer and Information Science, vol 1000. Springer, Cham. https://doi.org/10.1007/978-3-030-20257-6_33
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DOI: https://doi.org/10.1007/978-3-030-20257-6_33
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