Self-learning Monte Carlo method and cumulative update in fermion systems
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 ca...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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American Physical Society
2017
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| Online Access: | http://hdl.handle.net/1721.1/110003 https://orcid.org/0000-0001-8051-7349 https://orcid.org/0000-0002-8803-1017 |
| _version_ | 1811080943042035712 |
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| author | Liu, Junwei Shen, Huitao Qi, Yang Meng, Zi Yang Fu, Liang |
| author2 | Massachusetts Institute of Technology. Department of Physics |
| author_facet | Massachusetts Institute of Technology. Department of Physics Liu, Junwei Shen, Huitao Qi, Yang Meng, Zi Yang Fu, Liang |
| author_sort | Liu, Junwei |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T11:39:00Z |
| format | Article |
| id | mit-1721.1/110003 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:39:00Z |
| publishDate | 2017 |
| publisher | American Physical Society |
| record_format | dspace |
| spelling | mit-1721.1/1100032022-09-27T21:01:44Z Self-learning Monte Carlo method and cumulative update in fermion systems Liu, Junwei Shen, Huitao Qi, Yang Meng, Zi Yang Fu, Liang Massachusetts Institute of Technology. Department of Physics Liu, Junwei Shen, Huitao Qi, Yang Fu, Liang 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. 2017-06-19T13:45:12Z 2017-06-19T13:45:12Z 2017-06 2016-12 2017-06-07T22:00:05Z Article http://purl.org/eprint/type/JournalArticle 2469-9950 2469-9969 http://hdl.handle.net/1721.1/110003 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 https://orcid.org/0000-0001-8051-7349 https://orcid.org/0000-0002-8803-1017 en http://dx.doi.org/10.1103/PhysRevB.95.241104 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 | Liu, Junwei Shen, Huitao Qi, Yang Meng, Zi Yang Fu, Liang Self-learning Monte Carlo method and cumulative update in fermion systems |
| title | Self-learning Monte Carlo method and cumulative update in fermion systems |
| title_full | Self-learning Monte Carlo method and cumulative update in fermion systems |
| title_fullStr | Self-learning Monte Carlo method and cumulative update in fermion systems |
| title_full_unstemmed | Self-learning Monte Carlo method and cumulative update in fermion systems |
| title_short | Self-learning Monte Carlo method and cumulative update in fermion systems |
| title_sort | self learning monte carlo method and cumulative update in fermion systems |
| url | http://hdl.handle.net/1721.1/110003 https://orcid.org/0000-0001-8051-7349 https://orcid.org/0000-0002-8803-1017 |
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