Algorithms for faster and larger dynamic Metropolis simulations

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dc.creatorNovotny, M. A.
dc.creatorKolakowska, Alice K.
dc.creatorKorniss, G.
dc.date2003-11-05
dc.date.accessioned2026-07-07T02:54:36Z
dc.date.available2026-07-07T02:54:36Z
dc.descriptionIn dynamic Monte Carlo simulations, using for example the Metropolis dynamic, it is often required to simulate for long times and to simulate large systems. We present an overview of advanced algorithms to simulate for larger times and to simulate larger systems. The longer-time algorithm focused on is the Monte Carlo with Absorbing Markov Chains (MCAMC) algorithm. It is applied to metastability of an Ising model on a small-world network. Simulations of larger systems often require the use of non-trivial parallelization. Non-trivial parallelization of dynamic Monte Carlo is shown to allow perfectly scalable algorithms, and the theoretical efficiency of such algorithms is described.
dc.description8 pages, 3 figures, to appear in the AIP Conference Proceedings "The Monte Carlo method in physical sciences: celebrating the 50th anniversary of the Metropolis algorithm" edited by J. E. Gubernatis
dc.identifierhttps://arxiv.org/abs/cond-mat/0311108
dc.identifierhttp://arxiv.org/abs/cond-mat/0311108
dc.identifierAIP Conference Proceedings Vol. 690, 241 (2003)
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/22617
dc.subjectStatistical Mechanics
dc.subjectMaterials Science
dc.subjectComputational Physics
dc.titleAlgorithms for faster and larger dynamic Metropolis simulations
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