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Article
Worm Algorithms for Classical Statistical Models
Physics Review Letters
Publication Date
2001
Abstract
We show that high-temperature expansions provide a basis for the novel approach to efficient Monte Carlo simulations. “Worm” algorithms utilize the idea of updating closed-path configurations (produced by high-temperature expansions) through the motion of end points of a disconnected path. An amazing result is that local, Metropolis-type schemes using this approach appear to have dynamical critical exponents close to zero (i.e., their efficiency is comparable to the best cluster methods) as proved by finite-size scaling of the autocorrelation time for various universality classes.
Disciplines
Comments
This is the pre-published version harvested from ArXiv. The published version is located at http://prl.aps.org/abstract/PRL/v87/i16/e160601
Citation Information
Nikolai Prokof'ev and Boris Svistunov. "Worm Algorithms for Classical Statistical Models" Physics Review Letters Vol. 87 Iss. 16 (2001) Available at: http://works.bepress.com/nikolai_prokofev/39/