Self-Learning Monte Carlo Method: Continuous-Time Algorithm
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 continuous time Monte Carlo method with 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.
Cite
@article{arxiv.1705.06724,
title = {Self-Learning Monte Carlo Method: Continuous-Time Algorithm},
author = {Yuki Nagai and Huitao Shen and Yang Qi and Junwei Liu and Liang Fu},
journal= {arXiv preprint arXiv:1705.06724},
year = {2017}
}
Comments
6 pages, 5 figures + 2 page supplemental materials, to be published in Phys. Rev. B Rapid communication section