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Self-learning Monte Carlo method

Author(s)
Meng, Zi Yang; Liu, Junwei; Qi, Yang; Fu, Liang
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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.
Date issued
2017-01
URI
http://hdl.handle.net/1721.1/106311
Department
Massachusetts Institute of Technology. Department of Physics
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
ISSN
2469-9950
2469-9969

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