Incorporating Dynamic Mean-Field Theory into Diagrammatic Monte Carlo

Authors

L Pollet
Nikolai Prokof'ev, University of Massachusetts - AmherstFollow
Boris Svistunov, University of Massachusetts - AmherstFollow

Publication Date

2010

Abstract

The bold diagrammatic Monte Carlo (BDMC) method performs an unbiased sampling of Feyn- man's diagrammatic series using skeleton diagrams. For lattice models the eciency of BDMC can be dramatically improved by incorporating dynamic mean-eld theory solutions into renormalized propagators. From the DMFT perspective, combining it with BDCM leads to an unbiased method with well-dened accuracy. We illustrate the power of this approach by computing the single-particle propagator (and thus the density of states) in the non-perturbative regime of the Anderson local- ization problem, where a gain of the order of 104 is achieved with respect to conventional BDMC in terms of convergence to the exact answer.

Comments

This is the pre-published version harvested from ArXiv.

Recommended Citation

Pollet, L; Prokof'ev, Nikolai; and Svistunov, Boris, "Incorporating Dynamic Mean-Field Theory into Diagrammatic Monte Carlo" (2010). Physics Department Faculty Publication Series. 1179.
Retrieved from https://scholarworks.umass.edu/physics_faculty_pubs/1179