DSpace
 

RRI Digital Repository >
07. Theoretical Physics >
Research Papers (TP) >

Please use this identifier to cite or link to this item: http://hdl.handle.net/2289/4558

Title: Optimality of Wang-Landau sampling and performance limitations of flat histogram methods
Authors: Trebst, S.
Dayal, P.
Wessel, S.
Würtz, D.
Troyer, M.
Sabhapandit, Sanjib
Issue Date: 2003
Publisher: AIP conference Proceedings
Citation: The Monte Carlo Method in the Phydcal Sciences: Celebrating the 50th Anniversary of the Metropolis Algorithm. AIP Conference Proceedings, 2003, V.690
Abstract: We 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.
Description: Restricted Access. An open-access version is available at arXiv.org (one of the alternative locations)
URI: http://hdl.handle.net/2289/4558
ISSN: 0094-243X
Alternative Location: http://dx.doi.org/ 10.1063/1.1632165
http://adsabs.harvard.edu/abs/2003AIPC..690..400T
Copyright: 2003, AIP Conference Proceedings
Appears in Collections:Research Papers (TP)

Files in This Item:

File Description SizeFormat
2003_AIP Conference_V690_p400.pdfRestricted Access206.62 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

 

    RRI Library DSpace