Self-learning Monte Carlo method and cumulative update in fermion systems
Name
PhysRevB.95.241104.pdf
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218.21 KB
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Author(s)
Liu, Junwei
Shen, Huitao
Qi, Yang
Meng, Zi Yang
Fu, Liang
Date Issued
June 2017
Journal
Physical Review B
Publisher
American Physical Society
Citation
Liu, Junwei et al. “Self-Learning Monte Carlo Method and Cumulative Update in Fermion Systems.” Physical Review B 95.24 (2017): n. pag. © 2017 American Physical Society
Version
Final published version
Abstract
We develop the self-learning Monte Carlo (SLMC) method, a general-purpose numerical method recently introduced to simulate many-body systems, for studying interacting fermion systems. Our method uses a highly efficient update algorithm, which we design and dub “cumulative update”, to generate new candidate configurations in the Markov chain based on a self-learned bosonic effective model. From a general analysis and a numerical study of the double exchange model as an example, we find that the SLMC with cumulative update drastically reduces the computational cost of the simulation, while remaining statistically exact. Remarkably, its computational complexity is far less than the conventional algorithm with local updates.
MIT Department
Massachusetts Institute of Technology. Department of Physics
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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.95.241104
