Streaming Euclidean Max-Cut: Dimension vs Data Reduction

Max-Cut is a fundamental problem that has been studied extensively in various settings. We design an algorithm for Euclidean Max-Cut, where the input is a set of points in R-d in the model of dynamic geometric streams, where the input X subset of [Delta](d) is presented as a sequence of point insertions and deletions. Previously, Frahling and Sohler [STOC 2005] designed a (1+epsilon)-approximation algorithm for the low-dimensional regime, i.e., it uses space exp(d). To tackle this problem in the high-dimensional regime, which is of growing interest, one must improve the dependence on the dimension d, ideally to space complexity poly(epsilon(-1) d log Delta). Lammersen, Sidiropoulos, and Sohler [WADS 2009] proved that Euclidean Max-Cut admits dimension reduction with target dimension d' = poly(epsilon-1). Combining this with the aforementioned algorithm that uses space exp(d'), they obtain an algorithm whose overall space complexity is indeed polynomial in.., but unfortunately exponential in epsilon-1. We devise an alternative approach of data reduction, based on importance sampling, and achieve space bound poly(epsilon-1d log Delta), which is exponentially better (in) than the dimension-reduction approach. To implement this scheme in the streaming model, we employ a randomly-shifted quadtree to construct a tree embedding. While this is a well-known method, a key feature of our algorithm is that the embedding's distortion (.. log.) affects only the space complexity, and the approximation ratio remains 1 + epsilon-1