Selecting Initial States from Genetic Tempering for Efficient Monte Carlo Sampling
Abstract
An alternative to Monte Carlo techniques requiring large sampling times is presented here. Ideas from a genetic algorithm are used to select the best initial states from many independent, parallel MetropolisHastings iterations that are run on a single graphics processing unit. This algorithm represents the idealized limit of the parallel tempering method and, if the threads are selected perfectly, this algorithm converges without any Monte Carlo iterationsalthough some are required in practice. Models tested here (Ising, antiferromagnetic Kagome, and randombond Ising) are sampled on a time scale of seconds and with a small uncertainty that is free from autocorrelation.
 Publication:

arXiv eprints
 Pub Date:
 January 2018
 arXiv:
 arXiv:1801.09379
 Bibcode:
 2018arXiv180109379B
 Keywords:

 Condensed Matter  Statistical Mechanics
 EPrint:
 4 pages, 4 figures