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• W3102888917 abstract "We formulate an irreversible Markov chain Monte Carlo algorithm for the self-avoiding walk (SAW), which violates the detailed balance condition and satisfies the balance condition. Its performance improves significantly compared to that of the Berretti–Sokal algorithm, which is a variant of the Metropolis–Hastings method. The gained efficiency increases with spatial dimension (D), from approximately 10 times in 2D to approximately 40 times in 5D. We simulate the SAW on a 5D hypercubic lattice with periodic boundary conditions, for a linear system with a size up to L = 128, and confirm that as for the 5D Ising model, the finite-size scaling of the SAW is governed by renormalized exponents, v* = 2/d and γ/v* = d/2. The critical point is determined, which is approximately 8 times more precise than the best available estimate." @default.
• W3102888917 created "2020-11-23" @default.
• W3102888917 creator A5029616222 @default.
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• W3102888917 date "2016-12-22" @default.
• W3102888917 modified "2023-10-16" @default.
• W3102888917 title "Irreversible Markov chain Monte Carlo algorithm for self-avoiding walk" @default.
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• W3102888917 doi "https://doi.org/10.1007/s11467-016-0646-6" @default.
• W3102888917 hasPublicationYear "2016" @default.
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