Self-learning Monte Carlo method
Name
PhysRevB.95.041101.pdf
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473.82 KB
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Adobe PDF
Checksum (MD5)
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Author(s)
Meng, Zi Yang
Liu, Junwei
Qi, Yang
Fu, Liang
Date Issued
January 2017
Journal
Physical Review B
Publisher
American Physical Society
Citation
Liu, Junwei, et al. "Self-learning Monte Carlo method." Physical Review B, vol. 95, no. 041101, pp. 1-5. ©2017 American Physical Society.
Version
Final published version
Abstract
Monte Carlo simulation is an unbiased numerical tool for studying classical and quantum many-body systems. One of its bottlenecks is the lack of a general and efficient update algorithm for large size systems close to the phase transition, for which local updates perform badly. In this Rapid Communication, we propose a general-purpose Monte Carlo method, dubbed self-learning Monte Carlo (SLMC), in which an efficient update algorithm is first learned from the training data generated in trial simulations and then used to speed up the actual simulation. We demonstrate the efficiency of SLMC in a spin model at the phase transition point, achieving a 10–20 times speedup.
MIT Department
Massachusetts Institute of Technology. Department of Physics
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1103/PhysRevB.95.041101
