Selecting initial distributions of states for efficient Monte Carlo sampling*

A simple but massively parallel Monte Carlo method is demonstrated here [1]. Working with many different Monte Carlo samplers creates the opportunity to arrange the systems to partially cancel errors from insufficient relaxation. By averaging independent runs, auto-correlation is automatically canceled. This arrangement represents the idealized limit of parallel tempering. In order to determine an appropriate initial distribution, un-relaxed samples are randomly selected. Results from this method, called Genetic Tempering, for a variety of spin models are presented [2,3].

[1] T.E. Baker, arXiv:1801.09379
[2] C. Gauvin-Ndiaye, et. al. Phys. Rev. B 98, 125132 (2018)
[3] C. Gauvin-Ndiaye, A. Tremblay, and R. Nourafkan, arXiv:1809.07813