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TR15-131 | 10th August 2015 23:09

Smooth Boolean functions are easy: efficient algorithms for low-sensitivity functions


Authors: Parikshit Gopalan, Noam Nisan, Rocco Servedio, Kunal Talwar, Avi Wigderson
Publication: 12th August 2015 04:39
Downloads: 934


A natural measure of smoothness of a Boolean function is its sensitivity (the largest number of Hamming neighbors of a point which differ from it in function value). The structure of smooth or equivalently low-sensitivity functions is still a mystery. A well-known conjecture states that every such Boolean function can be computed by a shallow decision tree. While this conjecture implies that smooth functions are easy to compute in the simplest computational model, to date no non-trivial upper bounds were known for such functions in any computational model, including unrestricted Boolean circuits. Even a bound on the description length of such functions better than the trivial $2^n$ does not seem to have been known.

In this work, we establish the first computational upper bounds on smooth Boolean functions:

1) We show that every sensitivity $s$ function is uniquely specified by its values on a Hamming ball of radius $2s$. We use this to show that such functions can be computed by circuits of size $n^{O(s)}$.

2) We show that sensitivity $s$ functions satisfy a strong pointwise noise-stability guarantee for random noise of rate $O(1/s)$. We use this to show that these functions have formulas of depth $O(s\log n)$.

3) We show that sensitivity $s$ functions can be (locally) self-corrected from worst-case noise of rate $\exp(-O(s))$.

All our results are simple, and follow rather directly from (variants of) the basic fact that the function value at few points in small neighborhoods of a given point determine its function value via a majority vote. Our results confirm various consequences of the conjecture. They may be viewed as providing a new form of evidence towards its validity, as well as new directions towards attacking it.

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