Incorporating Dynamic Mean-Field Theory into Diagrammatic Monte Carlo

Details Incorporating Dynamic Mean-Field Theory into Diagrammatic Monte Carlo Incorporating Dynamic Mean-Field Theory into Diagrammatic Monte Carlo

2010-12-28

arxiv.org/abs/1012.5808v1

Phys. Rev. B 83, 161103(R) (2011)

Physics

Condensed Matter

Statistical Mechanics

4 pages, 3 figures

Scientific paper

The bold diagrammatic Monte Carlo (BDMC) method performs an unbiased sampling of Feynman's diagrammatic series using skeleton diagrams. For lattice models the efficiency of BDMC can be dramatically improved by incorporating dynamic mean-field theory (DMFT) solutions into renormalized propagators. From the DMFT perspective, combining it with BDCM leads to an unbiased method with well-defined 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 localization problem, where a gain of the order of $10^4$ is achieved with respect to conventional BDMC in terms of convergence to the exact answer.

Affiliated with

Also associated with

No associations

Rating

Incorporating Dynamic Mean-Field Theory into Diagrammatic Monte Carlo does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Incorporating Dynamic Mean-Field Theory into Diagrammatic Monte Carlo, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Incorporating Dynamic Mean-Field Theory into Diagrammatic Monte Carlo will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-428434