Lifted worm algorithm for the Ising model

Eren Metin Elçi, Jens Grimm, Lijie Ding, Abrahim Nasrawi, Timothy M. Garoni, Youjin Deng

Research output: Contribution to journalArticleResearchpeer-review

Abstract

We design an irreversible worm algorithm for the zero-field ferromagnetic Ising model by using the lifting technique. We study the dynamic critical behavior of an energylike observable on both the complete graph and toroidal grids, and compare our findings with reversible algorithms such as the Prokof'ev-Svistunov worm algorithm. Our results show that the lifted worm algorithm improves the dynamic exponent of the energylike observable on the complete graph and leads to a significant constant improvement on toroidal grids.

Original languageEnglish
Article number042126
Number of pages8
JournalPhysical Review E
Volume97
Issue number4
DOIs
Publication statusPublished - 18 Apr 2018

Cite this

Elçi, E. M., Grimm, J., Ding, L., Nasrawi, A., Garoni, T. M., & Deng, Y. (2018). Lifted worm algorithm for the Ising model. Physical Review E, 97(4), [042126]. https://doi.org/10.1103/PhysRevE.97.042126
Elçi, Eren Metin ; Grimm, Jens ; Ding, Lijie ; Nasrawi, Abrahim ; Garoni, Timothy M. ; Deng, Youjin. / Lifted worm algorithm for the Ising model. In: Physical Review E. 2018 ; Vol. 97, No. 4.
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Elçi, EM, Grimm, J, Ding, L, Nasrawi, A, Garoni, TM & Deng, Y 2018, 'Lifted worm algorithm for the Ising model', Physical Review E, vol. 97, no. 4, 042126. https://doi.org/10.1103/PhysRevE.97.042126

Lifted worm algorithm for the Ising model. / Elçi, Eren Metin; Grimm, Jens; Ding, Lijie; Nasrawi, Abrahim; Garoni, Timothy M.; Deng, Youjin.

In: Physical Review E, Vol. 97, No. 4, 042126, 18.04.2018.

Research output: Contribution to journalArticleResearchpeer-review

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AU - Elçi, Eren Metin

AU - Grimm, Jens

AU - Ding, Lijie

AU - Nasrawi, Abrahim

AU - Garoni, Timothy M.

AU - Deng, Youjin

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AB - We design an irreversible worm algorithm for the zero-field ferromagnetic Ising model by using the lifting technique. We study the dynamic critical behavior of an energylike observable on both the complete graph and toroidal grids, and compare our findings with reversible algorithms such as the Prokof'ev-Svistunov worm algorithm. Our results show that the lifted worm algorithm improves the dynamic exponent of the energylike observable on the complete graph and leads to a significant constant improvement on toroidal grids.

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Elçi EM, Grimm J, Ding L, Nasrawi A, Garoni TM, Deng Y. Lifted worm algorithm for the Ising model. Physical Review E. 2018 Apr 18;97(4). 042126. https://doi.org/10.1103/PhysRevE.97.042126