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The classical hypercontractive inequality for the noise operator on the discrete cube plays a crucial role in many of the fundamental results in the Analysis of Boolean functions, such as the KKL (Kahn-Kalai-Linial) theorem, Friedguts junta theorem and the invariance principle of Mossel, ODonnell and Oleszkiewicz. In these results the cube is equipped with the uniform ($1/2$-biased) measure, but it is desirable, particularly for applications to the theory of sharp thresholds, to also obtain such results for general $p$-biased measures. However, simple examples show that when $p$ is small there is no hypercontractive inequality that is strong enough for such applications. In this paper, we establish an effective hypercontractivity inequality for general $p$ that applies to `global functions, i.e. functions that are not significantly affected by a restriction of a small set of coordinates. This class of functions appears naturally, e.g. in Bourgains sharp threshold theorem, which states that such functions exhibit a sharp threshold. We demonstrate the power of our tool by strengthening Bourgains theorem, thereby making progress on a conjecture of Kahn and Kalai. An additional application of our hypercontractivity theorem, is a $p$-biased analog of the seminal invariance principle of Mossel, ODonnell, and Oleszkiewicz. In a companion paper, we give applications to the solution of two open problems in Extremal Combinatorics.
The hypercontractive inequality on the discrete cube plays a crucial role in many fundamental results in the Analysis of Boolean functions, such as the KKL theorem, Friedguts junta theorem and the invariance principle. In these results the cube is eq
If $G$ is a graph and $vec H$ is an oriented graph, we write $Gto vec H$ to say that every orientation of the edges of $G$ contains $vec H$ as a subdigraph. We consider the case in which $G=G(n,p)$, the binomial random graph. We determine the thresho
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