Abstract
A pair of complementary algorithms are presented. One is a fast method for inserting edges into a graph. The other is a fast method for removing edges from a graph. In concert, the algorithms confer the ability to arbitrarily modify any graph. Since the clusters of percolation models may be described as simple connected graphs, an efficient Monte Carlo scheme can be constructed where the occupation probability is swept back and forth between two turning points. This concentrates computational sampling time within the region of interest. A high precision value of $p_c = 0.59274603(9)$ was thus obtained.
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