We discuss the calculation of statistical averages of variables lying on S₁ or S₂ using (complex) Langevin equations. Assuming that the drift term is proportional to the gradient of a possibly complex function S({xᵢ}), xᵢ{∈}S₁ or S₂ we give the general form of such Langevin equations. These variables cause unphysical singularities and computational problems; thus we transform them to those of the embedding Euclidean space. We show in several examples that these modified (complex) Langevin equations have good convergence properties using an improved two-stage Runge-Kutta algorithm.
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Gausterer et al. (1986) studied this question.
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