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A Cubic Algorithm for Computing Gaussian Volume (1306.5829v2)

Published 25 Jun 2013 in cs.DS, math.FA, and math.PR

Abstract: We present randomized algorithms for sampling the standard Gaussian distribution restricted to a convex set and for estimating the Gaussian measure of a convex set, in the general membership oracle model. The complexity of integration is $O*(n3)$ while the complexity of sampling is $O*(n3)$ for the first sample and $O*(n2)$ for every subsequent sample. These bounds improve on the corresponding state-of-the-art by a factor of $n$. Our improvement comes from several aspects: better isoperimetry, smoother annealing, avoiding transformation to isotropic position and the use of the "speedy walk" in the analysis.

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