Deutsch
 
Benutzerhandbuch Datenschutzhinweis Impressum Kontakt
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Zeitschriftenartikel

Mixing and perfect sampling in one-dimensional particle systems

MPG-Autoren
/persons/resource/persons227492

Krauth,  Werner
Max Planck Institute for the Physics of Complex Systems, Max Planck Society;

Volltexte (frei zugänglich)

1806.06786.pdf
(Preprint), 910KB

Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Lei, z, Z., & Krauth, W. (2018). Mixing and perfect sampling in one-dimensional particle systems. EPL, 124(2): 20003. doi:10.1209/0295-5075/124/20003.


Zitierlink: http://hdl.handle.net/21.11116/0000-0002-A0B6-B
Zusammenfassung
We study the approach to equilibrium of the event-chain Monte Carlo (ECMC) algorithm for the one-dimensional hard-sphere model. Using the connection to the coupon-collector problem, we prove that a specific version of this local irreversible Markov chain realizes perfect sampling in O(N-2 log N) single steps, whereas the reversible local Metropolis algorithm requires O(N-3 log N) single steps for mixing. This confirms a special case of an earlier conjecture about O(N-2 log N) scaling of mixing times of ECMC and of the lifted forward Metropolis algorithm, its discretized variant. We also prove that sequential ECMC (with swaps) realizes perfect sampling in O(N-2) single events. Numerical simulations indicate a cross-over towards O(N-2 log N) mixing for the sequential forward swap Metropolis algorithm, that we introduce here. We point out open mathematical questions and possible applications of our findings to higher-dimensional models. Copyright (C) EFLA, 2018