Many problems in applied mathematics require the evaluation of the sum of N Gaussians at M points in space. The work required for direct evaluation grows like $NM$ as N and M increase; this makes it very expensive to carry out such calculations on a large scale. In this paper, an algorithm is presented which evaluates the sum of N Gaussians at M arbitrarily distributed points in C · (N + M) work, where C depends only on the precision required. When $N = M = 100,000$, the algorithm presented here is several thousand times faster than direct evaluation. It is based on a divide-and-conquer strategy, combined with the manipulation of Hermite expansions and Taylor series.
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Greengard et al. (1991) studied this question.
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