2016/07/31 by Takahiro Nemoto, Esteban Guevara Hidalgo, Vivien Lecomte · 1 citation
Physics and Astronomy · #cond-mat.stat-mech
paper · pdf · doi:10.1103/physreve.95.012102
published as Phys. Rev. E 95, 012102 (2017) · 13 pages, 1 figure. First part of pair of companion papers, Part II being arXiv:1607.08804
arxiv created 2017/05/10 · arxiv updated 2017/05/11
The Giardinà-Kurchan-Peliti algorithm is a numerical procedure that uses population dynamics in order to calculate large deviation functions associated to the distribution of time-averaged observables. To study the numerical errors of this algorithm, we explicitly devise a stochastic birth-death process that describes the time evolution of the population probability. From this formulation, we derive that systematic errors of the algorithm decrease proportionally to the inverse of the population size. Based on this observation, we propose a simple interpolation technique for the better estimation of large deviation functions. The approach we present is detailed explicitly in a two-state model.