Deterministic Approximate Counting for Juntas of Degree-$2$ Polynomial Threshold Functions

Abstract: Let $g: {-1,1}^k \to {-1,1}$ be any Boolean function and $q_1,\dots,q_k$ be any degree-2 polynomials over ${-1,1}<sup>n.$ We give a \emph{deterministic} algorithm which, given as input explicit descriptions of $g,q_1,\dots,q_k$ and an accuracy parameter $\eps&gt;0$, approximates [\Pr_{x \sim {-1,1}^n}[g(\sign(q_1(x)),\dots,\sign(q_k(x)))=1]] to within an additive $\pm \eps$. For any constant $\eps &gt; 0$ and $k \geq 1$ the running time of our algorithm is a fixed polynomial in $n$. This is the first fixed polynomial-time algorithm that can deterministically approximately count satisfying assignments of a natural class of depth-3 Boolean circuits. Our algorithm extends a recent result \cite{DDS13:deg2count} which gave a deterministic approximate counting algorithm for a single degree-2 polynomial threshold function $\sign(q(x)),$ corresponding to the $k=1$ case of our result. Our algorithm and analysis requires several novel technical ingredients that go significantly beyond the tools required to handle the $k=1$ case in \cite{DDS13:deg2count}. One of these is a new multidimensional central limit theorem for degree-2 polynomials in Gaussian random variables which builds on recent Malliavin-calculus-based results from probability theory. We use this CLT as the basis of a new decomposition technique for $k$-tuples of degree-2 Gaussian polynomials and thus obtain an efficient deterministic approximate counting algorithm for the Gaussian distribution. Finally, a third new ingredient is a "regularity lemma" for \emph{$k$-tuples} of degree-$d$ polynomial threshold functions. This generalizes both the regularity lemmas of \cite{DSTW:10,HKM:09} and the regularity lemma of Gopalan et al \cite{GOWZ10}. Our new regularity lemma lets us extend our deterministic approximate counting results from the Gaussian to the Boolean domain.