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Incorporating Dynamic Mean-Field Theory into Diagrammatic Monte Carlo
Physics Department Faculty Publication Series
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-dened 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.
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Comments
This is the pre-published version harvested from ArXiv.
Citation Information
L Pollet, Nikolai Prokof'ev and Boris Svistunov. "Incorporating Dynamic Mean-Field Theory into Diagrammatic Monte Carlo" (2010) Available at: http://works.bepress.com/boris_svistunov/38/