Nonlocal Monte Carlo algorithm for self-avoiding walks with fixed endpoints

We study a new Monte Carlo algorithm for generating self-avoiding walks with variable length (controlled by a fugacityβ) and fixed endpoints. The algorithm is a hybrid of local (BFACF) and nonlocal (cut-and-paste) moves. We find that the critical slowing-down, measured in units of computer time, is reduced compared to the pure BFACF algorithm:τCPU∼ 〈N〉≈2.3 versus 〈N〉≈3.0. We also prove some rigorous bounds on the autocorrelation time for these and related Monte Carlo algorithms.

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