Revision #2 Authors: Sourav Chakraborty, Raghav Kulkarni, Satyanarayana V. Lokam, Nitin Saurabh

Accepted on: 13th September 2018 14:49

Downloads: 65

Keywords:

Given a function $f : {0,1}^n \to \{+1,-1\}$, its {\em Fourier Entropy} is defined

to be $-\sum_S \widehat{f}(S)^2 \log \widehat{f}(S)^2$, where $\hat{f}$ denotes the

Fourier transform of $f$.

In the analysis of Boolean functions, an outstanding open question

is a conjecture of Friedgut and Kalai (1996), called the Fourier Entropy

Influence (FEI) Conjecture, asserting that the Fourier Entropy of any Boolean

function $f$ is bounded above, up to a constant factor, by the total influence (= average sensitivity) of $f$.

In this paper we give several upper bounds on the Fourier Entropy.

We first give upper bounds on the Fourier Entropy of Boolean functions in terms of several

complexity measures that are known to be bigger than the influence. These

complexity measures include, among others, the logarithm of the number of

leaves and the average depth of a parity decision tree. We then show that for

the class of Linear Threshold Functions (LTF), the Fourier Entropy is

$O(\sqrt{n})$. It is known that the average sensitivity for the class

of LTF is $\Theta(\sqrt{n})$. We also establish a bound of

$O_d(n^{1-\frac{1}{4d+6}})$ for general degree-$d$ polynomial threshold functions.

Our proof is based on a new upper bound on the

\emph{derivative of noise sensitivity}. Next we proceed to show that the

FEI Conjecture holds for read-once formulas that use $\mathsf{AND}$, $\mathsf{OR}$,

$\mathsf{XOR}$, and $\mathsf{NOT}$

gates. The last result is independent of a result due to

O'Donnell and Tan [OT'13] for read-once formulas with

\emph{arbitrary} gates of bounded fan-in, but our proof is completely

elementary and very different from theirs. Finally, we give a general bound

involving the first and second moments of sensitivities of a function

(average sensitivity being the first moment), which holds for real-valued

functions as well.

This is the full version of this paper. Inaccuracies from the previous version (in particular, Lemma 5.4 and Theorem 5.5) has been removed and more results are added.

Revision #1 Authors: Sourav Chakraborty, Raghav Kulkarni, Satyanarayana V. Lokam, Nitin Saurabh

Accepted on: 4th April 2013 05:51

Downloads: 1742

Keywords:

Given a function $f : \{0,1\}^n \to \reals$, its {\em Fourier Entropy} is defined to be $-\sum_S \fcsq{f}{S} \log \fcsq{f}{S}$, where $\fhat$ denotes the Fourier transform of $f$. This quantity arises in a number of applications, especially in the study of Boolean functions. An outstanding open question is a conjecture of Friedgut and Kalai (1996), called Fourier Entropy Influence (FEI) Conjecture, asserting that the Fourier Entropy of any Boolean function $f$ is bounded above,

up to a constant factor, by the total influence (= average sensitivity) of $f$.

In this paper we give several upper bounds on the Fourier Entropy of Boolean as well as

real valued functions. We give a general bound involving the $(1+\delta)$-th moment of $|S|$ w.r.t. the distribution $\fcsq{f}{S}$; the FEI conjecture needs the first moment of $|S|$. A variant of this bound uses the first and second moments of sensitivities (average sensitivity being the first moment). We also give upper bounds on the Fourier Entropy of Boolean functions in terms of several complexity measures that are known to be bigger than the influence. These complexity measures include, among others, the logarithm of the number of leaves and the average depth of a decision tree. Finally, we show that the FEI Conjecture holds for two special classes of functions, namely linear threshold functions and read-once formulas.

A curly bracket from the title is removed.

TR13-052 Authors: Sourav Chakraborty, Raghav Kulkarni, Satyanarayana V. Lokam, Nitin Saurabh

Publication: 3rd April 2013 23:01

Downloads: 1952

Keywords:

iven a function $f : \{0,1\}^n \to \reals$, its {\em Fourier Entropy} is defined to be $-\sum_S \fcsq{f}{S} \log \fcsq{f}{S}$, where $\fhat$ denotes the Fourier transform of $f$. This quantity arises in a number of applications, especially in the study of Boolean functions. An outstanding open question is a conjecture of Friedgut and Kalai (1996), called Fourier Entropy Influence (FEI) Conjecture, asserting that the Fourier Entropy of any Boolean function $f$ is bounded above,

up to a constant factor, by the total influence (= average sensitivity) of $f$.

In this paper we give several upper bounds on the Fourier Entropy of Boolean as well as

real valued functions. We give a general bound involving the $(1+\delta)$-th moment of $|S|$ w.r.t. the distribution $\fcsq{f}{S}$; the FEI conjecture needs the first moment of $|S|$. A variant of this bound uses the first and second moments of sensitivities (average sensitivity being the first moment). We also give upper bounds on the Fourier Entropy of Boolean functions in terms of several complexity measures that are known to be bigger than the influence. These complexity measures include, among others, the logarithm of the number of leaves and the average depth of a decision tree. Finally, we show that the FEI Conjecture holds for two special classes of functions, namely linear threshold functions and read-once formulas.