2007/02/28 by Nikolay Prokof’ev, Nikolay Prokof'ev, Boris Svistunov · 3 citations
Chemistry · Mathematics · Physics and Astronomy · #Advanced NMR Techniques and Applications #Applied mathematics #Computer science #Convergence (economics) #Diagrammatic reasoning #Dynamic Monte Carlo method #Hybrid Monte Carlo #Markov chain Monte Carlo #Mathematical analysis #Mathematics #Monte Carlo integration #Monte Carlo method #Monte Carlo method in statistical physics #Monte Carlo molecular modeling #Physics #Physics of Superconductivity and Magnetism #Quantum Monte Carlo #Quantum mechanics #Scattering #Sign (mathematics) #Spin (aerodynamics) #Statistical physics #Theoretical and Computational Physics #cond-mat.stat-mech #cond-mat.str-el
paper · pdf · doi:10.1103/physrevlett.99.250201
4 pages, 2 figures, revtex4
arxiv created 2007/09/22 · openalex publication_date 2007/12/18 · arxiv updated 2009/12/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
We introduce a Monte Carlo scheme for sampling a bold-line diagrammatic series specifying an unknown function in terms of itself. The range of convergence of this bold(-line) diagrammatic Monte Carlo (BMC) technique is significantly broader than that of a simple iterative scheme for solving integral equations. With the BMC technique, a moderate "sign problem" turns out to be an advantage in terms of the convergence of the process. For an illustrative purpose, we solve the one-particle s-scattering problem. As an important application, we obtain the T matrix for a Fermi polaron (one spin-down particle interacting with the spin-up fermionic sea).