Sample-Based High-Dimensional Convexity Testing
In the problem of high-dimensional convexity testing, there is an unknown set S in the n-dimensional Euclidean space which is promised to be either convex or c-far from every convex body with respect to the standard multivariate normal distribution. The job of a testing algorithm is then to distinguish between these two cases while making as few inspections of the set S as possible.
In this work we consider sample-based testing algorithms, in which the testing algorithm only has access to labeled samples (x,S(x)) where each x is independently drawn from the normal distribution. We give nearly matching sample complexity upper and lower bounds for both one-sided and two-sided convexity testing algorithms in this framework. For constant c, our results show that the sample complexity of one-sided convexity testing is exponential in n, while for two-sided convexity testing it is exponential in the square root of n.
Property testing
convexity
sample-based testing
37:1-37:20
Regular Paper
Xi
Chen
Xi Chen
Adam
Freilich
Adam Freilich
Rocco A.
Servedio
Rocco A. Servedio
Timothy
Sun
Timothy Sun
10.4230/LIPIcs.APPROX-RANDOM.2017.37
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