Performance of new algorithms for self-avoiding walks with fixed endpoints

Sergio Caracciolo; Andrea Pelissetto; Alan D. Sokal

doi:10.1016/0920-5632(89)90155-2

Performance of new algorithms for self-avoiding walks with fixed endpoints

Sergio Caracciolo, Andrea Pelissetto, Alan D. Sokal

Research output: Contribution to journal › Article › peer-review

Abstract

We discuss the dynamic critical behaviour of a hybrid Monte Carlo algorithm for the self-avoidings walks of variable length and fixed endpoints. A local algorithm is augmented by non-local cut-and-paste moves, which speed up equilibration within the subspace of walks with fixed length. The percentage of non-local moves can be optimized to get the lowest dynamic critical exponent for the autocorrelation time in CPU units.

Original language	English (US)
Pages (from-to)	525-528
Number of pages	4
Journal	Nuclear Physics B (Proceedings Supplements)
Volume	9
Issue number	C
DOIs	https://doi.org/10.1016/0920-5632(89)90155-2
State	Published - Jun 1989

ASJC Scopus subject areas

Atomic and Molecular Physics, and Optics
Nuclear and High Energy Physics

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10.1016/0920-5632(89)90155-2

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title = "Performance of new algorithms for self-avoiding walks with fixed endpoints",

abstract = "We discuss the dynamic critical behaviour of a hybrid Monte Carlo algorithm for the self-avoidings walks of variable length and fixed endpoints. A local algorithm is augmented by non-local cut-and-paste moves, which speed up equilibration within the subspace of walks with fixed length. The percentage of non-local moves can be optimized to get the lowest dynamic critical exponent for the autocorrelation time in CPU units.",

author = "Sergio Caracciolo and Andrea Pelissetto and Sokal, {Alan D.}",

note = "Funding Information: The research of one of the authors (A.D.S.) was supported in part by U.S. National Science Foundation grants PHY-8413569 and DMS-8705599. Copyright: Copyright 2014 Elsevier B.V., All rights reserved.",

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N2 - We discuss the dynamic critical behaviour of a hybrid Monte Carlo algorithm for the self-avoidings walks of variable length and fixed endpoints. A local algorithm is augmented by non-local cut-and-paste moves, which speed up equilibration within the subspace of walks with fixed length. The percentage of non-local moves can be optimized to get the lowest dynamic critical exponent for the autocorrelation time in CPU units.

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Performance of new algorithms for self-avoiding walks with fixed endpoints

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