Self-learning Monte Carlo method: Continuous-time algorithm

Nagai, Yuki; Shen, Huitao; Qi, Yang; Liu, Junwei; Fu, Liang

doi:10.1103/physrevb.96.161102

Title: Self-learning Monte Carlo method: Continuous-time algorithm

Journal Article · 03 October 2017 · Physical Review. B

DOI: https://doi.org/10.1103/physrevb.96.161102 · OSTI ID:1505622

Nagai, Yuki ^[1]; Shen, Huitao ^[2]; Qi, Yang ^[2]; Liu, Junwei ^[3]; Fu, Liang ^[2]

Japan Atomic Energy Agency, Chiba (Japan); Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); Hong Kong Univ. of Science and Technology, Hong Kong (China)

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. Furthermore, 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.

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Research Organization:: Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Materials Sciences & Engineering Division; USDOE

Grant/Contract Number:: SC0010526

OSTI ID:: 1505622

Alternate ID(s):: OSTI ID: 1396308

Journal Information:: Physical Review. B, Vol. 96, Issue 16; ISSN 2469-9950

Publisher:: American Physical Society (APS)Copyright Statement

Country of Publication:: United States

Language:: English

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