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Approximation of biased Boolean functions of small total influence by DNF's
The influence of the 'th coordinate on a Boolean function is the probability that flipping changes the value
. The total influence is the sum of influences of the coordinates.
The well-known `Junta Theorem' of Friedgut (1998) asserts that if , then can be -approximated by a function that depends on
coordinates. Friedgut's theorem has a wide variety of
applications in mathematics and theoretical computer science.
For a biased function with , the edge isoperimetric inequality on
the cube implies that . Kahn and Kalai (2006)
asked, in the spirit of the Junta theorem, whether any such that is
within a constant factor of the minimum, can be -approximated by
a DNF of a `small' size (i.e., a union of a small number of sub-cubes). We
answer the question by proving the following structure theorem: If , then can be -approximated by a DNF of
size . The dependence on is sharp up to the constant
factor in the double exponent.Comment: 14 page