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On the tensorization of the variational distance
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On the tensorization of the variational distance
abstract
If one seeks to estimate the total variation between two product measures $||P^\otimes_{1:n}-Q^\otimes_{1:n}||$ in terms of their marginal TV sequence $\delta=(||P_1-Q_1||,||P_2-Q_2||,\ldots,||P_n-Q_n||)$, then trivial upper and lower bounds are provided by$ ||\delta||_\infty \le ||P^\otimes_{1:n}-Q^\otimes_{1:n}||\le||\delta||_1$. We improve the lower bound to $||\delta||_2\lesssim||P^\otimes_{1:n}-Q^\otimes_{1:n}||$, thereby reducing the gap between the upper and lower bounds from $\sim n$ to $\sim\sqrt $. Furthermore, we show that {\em any} estimate on $||P^\otimes_{1:n}-Q^\otimes_{1:n}||$ expressed in terms of $\delta$ must necessarily exhibit a gap of $\sim\sqrt n$ between the upper and lower bounds in the worst case, establishing a sense in which our estimate is optimal. Finally, we identify a natural class of distributions for which $||\delta||_2$ approximates the TV distance up to absolute multiplicative constants.
Forward citations
Cited by 1 Pith paper
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On Computing Total Variation Distance Between Mixtures of Product Distributions
A randomized (1±ε)-approximation algorithm for TV distance between k-mixtures of product distributions runs in poly((nq)^k, 1/ε) time, with exact poly(n, 2^{O(k)}) deterministic algorithm for Boolean subcubes and #P-h...
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