%0 Book Section %1 statphys23_1010 %A Bellion, M. %A Santen, L. %A Rieger, H. %A Krauth, W. %B Abstract Book of the XXIII IUPAP International Conference on Statistical Physics %C Genova, Italy %D 2007 %E Pietronero, Luciano %E Loreto, Vittorio %E Zapperi, Stefano %K cluster collective montecarlo statphys23 topic-6 update %T Collective Monte Carlo update schemes for off-lattice systems %U http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=1010 %X The introduction of Cluster Monte Carlo algorithms led to much larger computational efficiency compared to (conventional) local Monte Carlo schemes. For example, by using cluster algorithms it was possible to reduce critical slowing down or even to avoid this problem. Unfortunately, up to now most such cluster algorithms have been designed for classical and quantum mechanical models that are defined on a lattice. In fact, currently there are very few cluster algorithms that work for off-lattice models. The so-called Geometric Cluster Algorithm (GCA) is based on the exchange of clusters generated by overlaying a rotated particle configuration with the original (non-rotated) configuration 1,2. Although this algorithm has been successfully applied to a number of model systems (phase separation in fluid mixtures, stabilisation of colloidal suspensions by nanoparticles, glass transition in a model glass former), it works rather inefficiently for higher densities. Here we present an alternative approach: The identification of particle clusters relies on the iteration of a translational elementary move. The main advantage of this approach is that the distribution of cluster sizes can be tuned by varying the step width of the elementary move. 1) C. Dress and W. Krauth, J. Phys. A 28, 597 (1995). \\ 2) J. Lui and E. Luijten, PRL 92, 035504 (2004).

@incollection{statphys23_1010, abstract = {The introduction of Cluster Monte Carlo algorithms led to much larger computational efficiency compared to (conventional) local Monte Carlo schemes. For example, by using cluster algorithms it was possible to reduce critical slowing down or even to avoid this problem. Unfortunately, up to now most such cluster algorithms have been designed for classical and quantum mechanical models that are defined on a lattice. In fact, currently there are very few cluster algorithms that work for off-lattice models. The so-called Geometric Cluster Algorithm (GCA) is based on the exchange of clusters generated by overlaying a rotated particle configuration with the original (non-rotated) configuration [1,2]. Although this algorithm has been successfully applied to a number of model systems (phase separation in fluid mixtures, stabilisation of colloidal suspensions by nanoparticles, glass transition in a model glass former), it works rather inefficiently for higher densities. Here we present an alternative approach: The identification of particle clusters relies on the iteration of a translational elementary move. The main advantage of this approach is that the distribution of cluster sizes can be tuned by varying the step width of the elementary move. 1) C. Dress and W. Krauth, J. Phys. A 28, 597 (1995). \\ 2) J. Lui and E. Luijten, PRL 92, 035504 (2004).}, added-at = {2007-06-20T10:16:09.000+0200}, address = {Genova, Italy}, author = {Bellion, M. and Santen, L. and Rieger, H. and Krauth, W.}, biburl = {https://www.bibsonomy.org/bibtex/2f9a4dffed0d34154921b39ea829430eb/statphys23}, booktitle = {Abstract Book of the XXIII IUPAP International Conference on Statistical Physics}, editor = {Pietronero, Luciano and Loreto, Vittorio and Zapperi, Stefano}, interhash = {35543405190724c7b34a75c54072a53b}, intrahash = {f9a4dffed0d34154921b39ea829430eb}, keywords = {cluster collective montecarlo statphys23 topic-6 update}, month = {9-13 July}, timestamp = {2007-06-20T10:16:36.000+0200}, title = {Collective Monte Carlo update schemes for off-lattice systems}, url = {http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=1010}, year = 2007 }