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Let S be a set of n points in ℝ^d, where d ≥ 2 is a constant, and let H₁,H₂,…,H_{m+1} be a sequence of vertical hyperplanes that are sorted by their first coordinates, such that exactly n/m points of S are between any two successive hyperplanes. Let |A(S,m)| be the number of different closest pairs in the {(m+1) choose 2} vertical slabs that are bounded by H_i and H_j, over all 1 ≤ i < j ≤ m+1. We prove tight bounds for the largest possible value of |A(S,m)|, over all point sets of size n, and for all values of 1 ≤ m ≤ n. As a result of these bounds, we obtain, for any constant ε > 0, a data structure of size O(n), such that for any vertical query slab Q, the closest pair in the set Q ∩ S can be reported in O(n^{1/2+ε}) time. Prior to this work, no linear space data structure with sublinear query time was known. 

Given a function f from the set [N] to a d-dimensional integer grid, we consider data structures that allow efficient orthogonal range searching queries in the image of f, without explicitly storing it. We show that, if f is of the form [N] → [2w]d for some w = polylog(N) and is computable in constant time, then, for any 0 < α < 1, we can obtain a data structure using Õ(N1-α/3) words of space such that, for a given d-dimensional axis-aligned box B, we can search for some x ∈ [N] such that f (x) ∈ B in time Õ(Nα). This result is obtained simply by combining integer range searching with the Fiat-Naor function inversion scheme, which was already used in data-structure problems previously. We further obtain • data structures for range counting and reporting, predecessor, selection, ranking queries, and combinations thereof, on the set f ([N]), • data structures for preimage size and preimage selection queries for a given value of f, and • data structures for selection and ranking queries on geometric quantities computed from tuples of points in d-space. These results unify and generalize previously known results on 3SUM-indexing and string searching, and are widely applicable as a black box to a variety of problems. In particular, we give a data structure for a generalized version of gapped string indexing, and show how to preprocess a set of points on an integer grid in order to efficiently compute (in sublinear time), for points contained in a given axis-aligned box, their Theil-Sen estimator, the kth largest area triangle, or the induced hyperplane that is the kth furthest from the origin. 

We present subquadratic algorithms in the algebraic decision-tree model for several 3Sum-hard geometric problems, all of which can be reduced to the following question: Given two sets A, B, each consisting of n pairwise disjoint segments in the plane, and a set C of n triangles in the plane, we want to count, for each triangle Δ ∈ C, the number of intersection points between the segments of A and those of B that lie in Δ. The problems considered in this paper have been studied by Chan (2020), who gave algorithms that solve them, in the standard real-RAM model, in O((n²/log²n) log^O(1) log n) time. We present solutions in the algebraic decision-tree model whose cost is O(n^{60/31+ε}), for any ε > 0. Our approach is based on a primal-dual range searching mechanism, which exploits the multi-level polynomial partitioning machinery recently developed by Agarwal, Aronov, Ezra, and Zahl (2020). A key step in the procedure is a variant of point location in arrangements, say of lines in the plane, which is based solely on the order type of the lines, a "handicap" that turns out to be beneficial for speeding up our algorithm. 

We study the relationship between memory accesses, bank conflicts, thread multiplicity (also known as over-subscription) and instruction-level parallelism in comparison-based sort- ing algorithms for Graphics Processing Units (GPUs). We experimentally validate a proposed formula that relates these parameters with asymptotic analysis of the number of mem- ory accesses by an algorithm. Using this formula we analyze and compare several GPU sorting algorithms, identifying key performance bottlenecks in each one of them. Based on this analysis we propose a GPU-efficient multiway merge- sort algorithm, GPU-MMS, which minimizes or eliminates these bottlenecks and balances various limiting factors for specific hardware. We realize an implementation of GPU-MMS and compare it to sorting algorithm implementations in state-of-the-art GPU libraries on three GPU architectures. Despite these library implementations being highly optimized, we find that GPU-MMS outperforms them by an average of 21% for random integer inputs and 14% for random key-value pairs.