Lifted Worm Algorithm for the Ising Model
arXiv:1711.05346 · doi:10.1103/PhysRevE.97.042126
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 energy estimator 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 energy estimator on the complete graph, and leads to a significant constant improvement on toroidal grids.
9 pages, 6 figures
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- Worm-algorithm-type Simulation of Quantum Transverse-Field Ising Model
- Loop-Cluster Coupling and Algorithm for Classical Statistical Models
- Logarithmic finite-size scaling of the self-avoiding walk at four dimensions
- Geometric properties of the complete-graph Ising model in the loop representation
- Geometric allocation approach to accelerating directed worm algorithm
- Lifted directed-worm algorithm
- Finite-Size Scaling of the High-Dimensional Ising Model in the Loop Representation
- Logarithmic Finite-Size Scaling of the Four-Dimensional Ising Model
- Graphical Representations and Worm Algorithms for the O() Spin Model