Self-learning Monte Carlo method: Continuous-time algorithm
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...
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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American Physical Society
2018
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| Online Access: | http://hdl.handle.net/1721.1/114482 https://orcid.org/0000-0001-8051-7349 https://orcid.org/0000-0002-8803-1017 |
| _version_ | 1826207580841574400 |
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| author | Nagai, Yuki Shen, Huitao Qi, Yang Liu, Junwei Fu, Liang |
| author2 | Massachusetts Institute of Technology. Department of Physics |
| author_facet | Massachusetts Institute of Technology. Department of Physics Nagai, Yuki Shen, Huitao Qi, Yang Liu, Junwei Fu, Liang |
| author_sort | Nagai, Yuki |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T13:51:46Z |
| format | Article |
| id | mit-1721.1/114482 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T13:51:46Z |
| publishDate | 2018 |
| publisher | American Physical Society |
| record_format | dspace |
| spelling | mit-1721.1/1144822022-10-01T17:36:51Z Self-learning Monte Carlo method: Continuous-time algorithm Nagai, Yuki Shen, Huitao Qi, Yang Liu, Junwei Fu, Liang Massachusetts Institute of Technology. Department of Physics Nagai, Yuki Shen, Huitao Qi, Yang Liu, Junwei Fu, Liang 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. United States. Department of Energy. Office of Basic Energy Sciences (Award DE-SC0010526) 2018-03-30T17:49:38Z 2018-03-30T17:49:38Z 2017-10 2017-05 2017-11-14T22:45:14Z Article http://purl.org/eprint/type/JournalArticle 2469-9950 2469-9969 http://hdl.handle.net/1721.1/114482 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 https://orcid.org/0000-0001-8051-7349 https://orcid.org/0000-0002-8803-1017 en http://dx.doi.org/10.1103/PhysRevB.96.161102 Physical Review B 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. American Physical Society application/pdf American Physical Society American Physical Society |
| spellingShingle | Nagai, Yuki Shen, Huitao Qi, Yang Liu, Junwei Fu, Liang Self-learning Monte Carlo method: Continuous-time algorithm |
| title | Self-learning Monte Carlo method: Continuous-time algorithm |
| title_full | Self-learning Monte Carlo method: Continuous-time algorithm |
| title_fullStr | Self-learning Monte Carlo method: Continuous-time algorithm |
| title_full_unstemmed | Self-learning Monte Carlo method: Continuous-time algorithm |
| title_short | Self-learning Monte Carlo method: Continuous-time algorithm |
| title_sort | self learning monte carlo method continuous time algorithm |
| url | http://hdl.handle.net/1721.1/114482 https://orcid.org/0000-0001-8051-7349 https://orcid.org/0000-0002-8803-1017 |
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