Probabilistic Polynomials and Hamming Nearest Neighbors

• Published in: 2015 IEEE 56th Annual Symposium on Foundations of Computer Science pp. 136 - 150
• Main Authors: Alman, Josh, Williams, Ryan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2015
• Subjects: Approximation algorithms; Approximation methods; Boolean functions; Hamming distance; nearest neighbors; Polynomials; Probabilistic logic; probabilistic polynomials
• ISSN: 0272-5428
• DOI: 10.1109/FOCS.2015.18

We show how to compute any symmetric Boolean function on n variables over any field (as well as '/ the integers) with a probabilistic polynomial of degree O( √nlog(1/ε)) and error at most ε. The degree dependence on n and ε is optimal, matching a lower bound of Razborov (1987) and Smolensky (1987) for the MAJORITY function. The proof is constructive: a low-degree polynomial can be efficiently sampled from the distribution. This polynomial construction is combined with other algebraic ideas to give the first subquadratic time algorithm for computing a (worst-case) batch of Hamming distances in superlogarithmic dimensions, exactly. To illustrate, let c(n) : ℕ → ℕ. Suppose we are given a database D of n vectors in {0,1} c(n)logn and a collection of n query vectors Q in the same dimension. For all u ∈ Q, we wish to compute a v ∈ D with minimum Hamming distance from u. We solve this problem in n 2-1/O(c(n)log 2 c(n)) randomized time. Hence, the problem is in "truly subquadratic" time for O(logn) dimensions, and in subquadratic time for d = o((log2 n)/(loglogn) 2 ). We apply the algorithm to computing pairs with maximum inner product, closest pair in ℓ1 for vectors with bounded integer entries, and pairs with maximum Jaccard coefficients.