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Please use this identifier to cite or link to this item: http://hdl.handle.net/2289/4558
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dc.contributor.authorTrebst, S.-
dc.contributor.authorDayal, P.-
dc.contributor.authorWessel, S.-
dc.contributor.authorWurtz, D.-
dc.contributor.authorTroyer, M.-
dc.contributor.authorSabhapandit, Sanjib-
dc.date.accessioned2012-05-30T06:07:53Z-
dc.date.available2012-05-30T06:07:53Z-
dc.date.issued2003-
dc.identifier.citationThe Monte Carlo Method in the Phydcal Sciences: Celebrating the 50th Anniversary of the Metropolis Algorithm. AIP Conference Proceedings, 2003, V.690en
dc.identifier.issn0094-243X-
dc.identifier.urihttp://hdl.handle.net/2289/4558-
dc.descriptionRestricted Access. An open-access version is available at arXiv.org (one of the alternative locations)en
dc.description.abstractWe determine the optimal scaling of local-update flat-histogram methods (such as multicanonical, broad histograms, tempering, or Wang-Landau sampling) with system size by using a perfect flat-histogram scheme based on the exact density of states of 2D Ising models. We find that the Wang-Landau algorithm shows the same scaling as the perfect scheme and is thus optimal. However, even for the perfect scheme the scaling with the number of spins N is slower than the minimal N2 of an unbiased random walk in energy space for both, local and N-fold way updates. While it still follows a power law N2Lz for the ferromagnetic and fully frustrated Ising model we find exponential scaling of the tunneling times in the case of the +/-J Ising spin glass. The tunneling times of the +/-J Ising spin glass are found to follow a fat-tailed Fréchet extremal value distribution.en
dc.language.isoenen
dc.publisherAIP conference Proceedingsen
dc.relation.urihttp://dx.doi.org/ 10.1063/1.1632165en
dc.relation.urihttp://adsabs.harvard.edu/abs/2003AIPC..690..400Ten
dc.rights2003, AIP Conference Proceedingsen
dc.titleOptimality of Wang-Landau sampling and performance limitations of flat histogram methodsen
dc.typeArticleen
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