Self-learning Monte Carlo method: Continuous-time algorithm
Name
PhysRevB.96.161102.pdf
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308.7 KB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Nagai, Yuki
Shen, Huitao
Qi, Yang
Liu, Junwei
Fu, Liang
Date Issued
October 2017
Journal
Physical Review B
Publisher
American Physical Society
Citation
Nagai, Yuki et al. "Self-learning Monte Carlo method: Continuous-time algorithm." Physical Review B 96, 16 (October 2017): 161102(R) © 2017 American Physical Society
Version
Final published version
Abstract
The recently introduced self-learning Monte Carlo method is a general-purpose numerical method that speeds up Monte Carlo simulations by training an effective model to propose uncorrelated configurations in the Markov chain. We implement this method in the framework of a continuous-time Monte Carlo method with an auxiliary field in quantum impurity models. We introduce and train a diagram generating function (DGF) to model the probability distribution of auxiliary field configurations in continuous imaginary time, at all orders of diagrammatic expansion. By using DGF to propose global moves in configuration space, we show that the self-learning continuous-time Monte Carlo method can significantly reduce the computational complexity of the simulation.
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.96.161102
