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Please use this identifier to cite or link to this item: http://hdl.handle.net/2289/4521
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dc.contributor.authorDayal, P.-
dc.contributor.authorTrebst, S.-
dc.contributor.authorWessel, S.-
dc.contributor.authorWurtz, D.-
dc.contributor.authorTroyer, M.-
dc.contributor.authorSabhapandit, Sanjib-
dc.contributor.authorCoppersmith, S.N.-
dc.date.accessioned2012-05-24T03:45:55Z-
dc.date.available2012-05-24T03:45:55Z-
dc.date.issued2004-03-
dc.identifier.citationPhysical Review Letters, 2004, Vol.92, p097201en
dc.identifier.issn0031-9007-
dc.identifier.issn1079-7114 (Online)-
dc.identifier.urihttp://hdl.handle.net/2289/4521-
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 with system size by using a perfect flat-histogram scheme based upon the exact density of states of 2D Ising models. The typical tunneling time needed to sample the entire bandwidth does not scale with the number of spins N as the minimal N2 of an unbiased random walk in energy space. While the scaling is power law for the ferromagnetic and fully frustrated Ising model, for the ±J nearest-neighbor spin glass the distribution of tunneling times is governed by a fat-tailed Fréchet extremal value distribution that obeys exponential scaling. Furthermore, the shape parameters of these distributions indicate that statistical sample means become ill defined already for moderate system sizes within these complex energy landscapes.en
dc.language.isoenen
dc.publisherAmerican Physical Societyen
dc.relation.urihttp://arxiv.org/abs/cond-mat/0306108en
dc.relation.urihttp://dx.doi.org/10.1103/PhysRevLett.92.097201en
dc.relation.urihttp://adsabs.harvard.edu/abs/2004PhRvL..92i7201Den
dc.rights2004 The American Physical Societyen
dc.subjectclassical spin modelsen
dc.subjectgeneral statistical methodsen
dc.subjectorder-disorder transformationsen
dc.subjectstatistical mechanics of model systemsen
dc.titlePerformance limitations of flat-histogram methodsen
dc.typeArticleen
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