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We study some aspects of a Monte Carlo method invented by Maggs and Rossetto for simulating systems of charged particles. It has the feature that the discretized electric field is updated locally when charges move. Results of simulations of the two dimensional one-component plasma are presented. Highly accurate results can be obtained very efficiently using this lattice method over a large temperature range. The method differs from global methods in having additional degrees of freedom which leads to the question of how a faster method can result. We argue that efficient sampling depends on charge mobility and find that the mobility is close to maximum for a low rate of independent plaquette updates for intermediate temperatures. We present a simple model to account for this behavior. We also report on the role of uniform electric field sampling using this method.
Many problems in materials science and biology involve particles interacting with strong, short-ranged bonds, that can break and form on experimental timescales. Treating such bonds as constraints can significantly speed up sampling their equilibrium
The hard-disk problem, the statics and the dynamics of equal two-dimensional hard spheres in a periodic box, has had a profound influence on statistical and computational physics. Markov-chain Monte Carlo and molecular dynamics were first discussed f
P.B. Chakraborty {it et al.}, Phys. Rev. B {bf 70}, 144411 (2004)) study of the LiHoF$_4$ Ising magnetic material in an external transverse magnetic field $B_x$ show a discrepancy with the experimental results, even for small $B_x$ where quantum fluc
Active Brownian particles (ABPs) and Run-and-Tumble particles (RTPs) both self-propel at fixed speed $v$ along a body-axis ${bf u}$ that reorients either through slow angular diffusion (ABPs) or sudden complete randomisation (RTPs). We compare the ph
We review efficient Monte Carlo methods for simulating quantum systems which couple to a dissipative environment. A brief introduction of the Caldeira-Leggett model and the Monte Carlo method will be followed by a detailed discussion of cluster algor