# Geometric allocation approach to accelerating directed worm algorithm

## TL;DR

This paper introduces a directed worm algorithm using geometric allocation to significantly improve Monte Carlo simulation efficiency, outperforming traditional methods and even the Wolff cluster algorithm in certain models.

## Contribution

The authors propose a novel directed worm algorithm with geometric allocation that enhances efficiency and diffusivity, outperforming existing algorithms like the Wolff cluster method.

## Key findings

- Approximately 25 times more efficient than conventional worm update.
- Surpasses the Wolff cluster algorithm in efficiency for the simple cubic lattice model.
- Estimates the dynamic critical exponent as z ≈ 0.27 for the 3D Ising model.

## Abstract

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