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Worm algorithms for classical statistical models

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arxiv cond-mat/0103146 v1 pith:AZB33TIE submitted 2001-03-06 cond-mat

Nikolay Prokof'ev , Boris Svistunov This is my paper · ORCID
classification cond-mat
keywords algorithmsexpansionshigh-temperaturepathwormamazingapproachautocorrelation
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We show that high-temperature expansions may serve as 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 may have dynamical critical exponents close to zero (i.e., their efficiency is comparable to the best cluster methods). We demonstrate this by calculating finite size scaling of the autocorrelation time for various (six) universality classes.

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Cited by 3 Pith papers

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  3. Symmetry-preserving neural networks in lattice field theories

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