Sewing Algorithm
T. E. Booth, J. E. Gubernatis
TL;DR
The paper introduces the sewing algorithm, a method that allows Monte Carlo sampling of large system states using samples from smaller systems, demonstrated on the 2D Ising model.
Contribution
It presents a novel sewing algorithm that facilitates efficient Monte Carlo sampling for large systems based on smaller system samples.
Findings
Effective sampling of large system states demonstrated
Applicable to transfer matrix methods in statistical physics
Reduces computational complexity for large systems
Abstract
We present a procedure that in many cases enables the Monte Carlo sampling of states of a large system from the sampling of states of a smaller system. We illustrate this procedure, which we call the sewing algorithm, for sampling states from the transfer matrix of the two-dimensional Ising model.
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