Geometric allocation approach to accelerating directed worm algorithm

نویسندگان

چکیده

The worm algorithm is a versatile technique in the Markov chain Monte Carlo method for both classical and quantum systems. substantially alleviates critical slowing down reduces dynamic exponents of various It crucial to improve push boundary physical We here propose directed that significantly improves computational efficiency. use geometric allocation approach optimize scattering process: backscattering averted, forward favored. Our successfully enhances diffusivity head (kink), which evident probability distribution relative position two kinks. Performance improvement demonstrated Ising model at temperature by measurement exponential autocorrelation times asymptotic variances. present update approximately 25 as efficient conventional simple cubic lattice model. Surprisingly, our even more than Wolff cluster algorithm, one best algorithms. estimate exponent be $z \approx 0.27$ update. algorithms produce different integrated time magnetic susceptibility estimator but same variance. also discuss how quantify efficiency method. can applied wide range systems, such $| \phi |^4$ model, Potts O($n$) loop QCD.

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ژورنال

عنوان ژورنال: Physical Review E

سال: 2021

ISSN: ['1550-2376', '1539-3755']

DOI: https://doi.org/10.1103/physreve.103.013308