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 language | English |
|---|---|
| Article number | 042126 |
| Journal | Physical Review E - Statistical, Nonlinear, and Soft Matter Physics |
| Volume | 97 |
| Issue number | 4 |
| DOIs | |
| State | Published - Apr 18 2018 |
| Externally published | Yes |
Funding
We would like to thank Zongzheng Zhou for fruitful discussions. E.M.E. is grateful to Nikolaos G. Fytas for fruitful discussions regarding various finite-size scaling Ansätze . This work was supported under the Australian Research Council's Discovery Projects funding scheme (Project No. DP140100559). It was undertaken with the assistance of resources from the National Computational Infrastructure (NCI), which is supported by the Australian Government. Furthermore, we would like to acknowledge the Monash eResearch Centre and eSolutions-Research Support Services through the use of the Monash Campus HPC Cluster. Y.D. thanks the National Natural Science Foundation of China for their support under Grant No. 11625522 and the Fundamental Research Funds for the Central Universities under Grant No. 2340000034. Y.D. also thanks MOST for their support under Grant No. 2016YFA0301604. We would like to thank Zongzheng Zhou for fruitful discussions. E.M.E. is grateful to Nikolaos G. Fytas for fruitful discussions regarding various finite-size scaling Ansätze. This work was supported under the Australian Research Council's Discovery Projects funding scheme (Project No. DP140100559). It was undertaken with the assistance of resources from the National Computational Infrastructure (NCI), which is supported by the Australian Government. Furthermore, we would like to acknowledge the Monash eResearch Centre and eSolutions-Research Support Services through the use of the Monash Campus HPC Cluster. Y.D. thanks the National Natural Science Foundation of China for their support under Grant No. 11625522 and the Fundamental Research Funds for the Central Universities under Grant No. 2340000034. Y.D. also thanks MOST for their support under Grant No. 2016YFA0301604.