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}^n. We give a deterministic algorithm which, given as input explicit descriptions of g, q_1, dots, q_k and an accuracy parameter eps>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 > 0 and k geq 1 the running time of our algorithm is a fixed polynomial in n (in fact this is true even for some not-too-small eps = o_n(1) and not-too-large k = omega_n(1)). 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 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. Note that even in the k=1 case it is NP-hard to determine whether Pr_{x sim {-1, 1}^n}[sign(q(x))=1] is nonzero, so any sort of multiplicative approximation is almost certainly impossible even for efficient randomized algorithms. 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, i.e., an algorithm for estimating [ Pr_{x sim N(0, 1)^n}[g(sign(q_1(x)), dots, sign(q_k(x)))=1]. ] Finally, a third new ingredient is a for k-tuples of degree-d polynomial threshold functions. This generalizes both the regularity lemmas of DSTW:10, HKM:09 (which apply to a single degree-d polynomial threshold function) and the regularity lemma of Gopalan et al GOWZ10 (which applies to a k-tuples of linear threshold functions, i.e., the case d=1). Our new regularity lemma lets us extend our deterministic approximate counting results from the Gaussian to the Boolean domain.