In this paper we establish an intimate connection between dynamic range searching in the group model and combinatorial discrepancy. Our result states that, for a broad class of range searching data structures (including all known upper bounds), it must hold that $t_u t_q=\Omega(\mbox{disc}^2)$, where $t_u$ is the worst case update time, $t_q$ is the worst case query time, and disc is the combinatorial discrepancy of the range searching problem in question. This relation immediately implies a whole range of exceptionally high and near-tight lower bounds for all of the basic range searching problems. We list a few of them in the following: (1) For $d$-dimensional halfspace range searching, we get a lower bound of $t_u t_q=\Omega(n^{1-1/d})$. This comes within an lg lg $n$ factor of the best known upper bound. (2) For orthogonal range searching, we get a lower bound of $t_u t_q=\Omega(\mbox{lg}^{d-1}n)$. (3) For ball range searching, we get a lower bound of $t_u t_q=\Omega(n^{1-1/d})$. We note that the previous highest lower bound for any explicit problem, due to Pǎtraşcu [Proceedings of the 39th ACM Symposium on Theory of Computation, 2007, pp. 40--46], states that $t_q=\Omega((\mbox{lg} n/\mbox{lg}(\mbox{lg} n+t_u))^2)$, which does, however, hold for a less restrictive class of data structures. Our result also has implications for the field of combinatorial discrepancy. Using textbook range searching solutions, we improve on the best known discrepancy upper bound for axis-aligned rectangles in all dimensions


Read More: http://epubs.siam.org/doi/abs/10.1137/120865240