Sampling the Fermi statistics and other conditional product measures
Annales de l'I.H.P. Probabilités et statistiques, Volume 47 (2011) no. 3, pp. 790-812.

Through a Metropolis-like algorithm with single step computational cost of order one, we build a Markov chain that relaxes to the canonical Fermi statistics for k non-interacting particles among m energy levels. Uniformly over the temperature as well as the energy values and degeneracies of the energy levels we give an explicit upper bound with leading term km ln k for the mixing time of the dynamics. We obtain such construction and upper bound as a special case of a general result on (non-homogeneous) products of ultra log-concave measures (like binomial or Poisson laws) with a global constraint. As a consequence of this general result we also obtain a disorder-independent upper bound on the mixing time of a simple exclusion process on the complete graph with site disorder. This general result is based on an elementary coupling argument, illustrated in a simulation appendix and extended to (non-homogeneous) products of log-concave measures.

En définissant un algorithme de type Metropolis dont le coût de chaque pas est d'ordre 1, nous construisons une chaîne de Markov dont la mesure d'équilibre est donnée par la statistique de Fermi canonique pour k particules sans interaction parmi m niveaux d'énergie. Uniformément en la température, ainsi qu'en les énergies et capacités des différents niveaux d'énergie, nous donnons une majoration explicite et de terme dominant km ln k du temps de mélange de la dynamique. Nous obtenons cette construction et cette majoration comme cas particulier d'un résultat général sur les produits (non homogènes) de mesures ultra log-concaves (comme les lois binômiales ou de Poisson) sous une contrainte globale. Ce résultat général fournit aussi une majoration indépendante du désordre pour le temps de mélange du processus d'exclusion simple sur le graphe complet en potentiel aléatoire. Il découle d'un argument de couplage élémentaire, est illustré dans une appendice de simulations et étendu aux produits (non homogènes) de mesures log-concaves.

DOI: 10.1214/10-AIHP385
Classification: 60J10,  82C44
Keywords: metropolis algorithm, Markov chain, sampling, mixing time, product measure, conservative dynamics
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Gaudillière, A.; Reygner, J. Sampling the Fermi statistics and other conditional product measures. Annales de l'I.H.P. Probabilités et statistiques, Volume 47 (2011) no. 3, pp. 790-812. doi : 10.1214/10-AIHP385. http://www.numdam.org/articles/10.1214/10-AIHP385/

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