Abstract
Computing the convolution A ⋆ B of two length-n vectors A,B is an ubiquitous computational primitive, with applications in a variety of disciplines. Within theoretical computer science, applications range from string problems to Knapsack-type problems, and from 3SUM to All-Pairs Shortest Paths. These applications often come in the form of nonnegative convolution, where the entries of A,B are nonnegative integers. The classical algorithm to compute A⋆ B uses the Fast Fourier Transform (FFT) and runs in time O(n logn).
However, in many cases A and B might satisfy sparsity conditions, and hence one could hope for significant gains compared to the standard FFT algorithm. The ideal goal would be an O(k logk)-time algorithm, where k is the number of non-zero elements in the output, i.e., the size of the support of A ⋆ B. This problem is referred to as sparse nonnegative convolution, and has received a considerable amount of attention in the literature; the fastest algorithms to date run in time O(k log2 n).
The main result of this paper is the first O(k logk)-time algorithm for sparse nonnegative convolution. Our algorithm is randomized and assumes that the length n and the largest entry of A and B are subexponential in k. Surprisingly, we can phrase our algorithm as a reduction from the sparse case to the dense case of nonnegative convolution, showing that, under some mild assumptions, sparse nonnegative convolution is equivalent to dense nonnegative convolution for constant-error randomized algorithms. Specifically, if D(n) is the time to convolve two nonnegative length-n vectors with success probability 2/3, and S(k) is the time to convolve two nonnegative vectors with output size k with success probability 2/3, then S(k) = O(D(k) + k (loglogk)2).
Our approach uses a variety of new techniques in combination with some old machinery from linear sketching and structured linear algebra, as well as new insights on linear hashing, the most classical hash function.
