Abstract

Motivated by the problems of computing sample covariance matrices, and of transforming a collection of vectors to a basis where they are sparse, we present a simple algorithm that computes an approximation of the product of two n-by-n real matrices A and B. Let ||AB||F denote the Frobenius norm of AB, and b be a parameter determining the time/accuracy trade-off. Given 2-wise independent hash functions h1, h2: [n] → [b], and s1, s2: [n] → {−1, +1} the algorithm works by first “compressing ” the matrix product into the polynomial n∑ n∑ p(x) = Aiks1(i) x h1(i) n∑ Bkjs2(j) x h2(j) k=1 i=1 j=1 Using FFT for polynomial multiplication, we can compute c0,..., cb−1 such that ∑ i cixi = (p(x) mod x b)+(p(x) div x b) in time Õ(n2 + nb). An unbiased estimator of (AB)ij with variance at most ||AB| | 2 F /b can then be computed as: Cij = s1(i) s2(j) c(h1(i)+h2(j)) mod b. Our approach also leads to an algorithm for computing AB exactly, whp., in time Õ(N + nb) in the case where A and B have at most N nonzero entries, and AB has at most b nonzero entries. Also, we use error-correcting codes in a novel way to recover significant entries of AB in near-linear time.

Citations