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On Semialgebraic Range Reporting

Published 14 Mar 2022 in cs.CG | (2203.07096v2)

Abstract: In the problem of semialgebraic range searching, we are to preprocess a set of points in R<sup>D\mathbb{R}<sup>D such that the subset of points inside a semialgebraic region described by O(1)O(1) polynomial inequalities of degree Δ\Delta can be found efficiently. Relatively recently, several major advances were made on this problem. Using algebraic techniques, "near-linear space" structures [AMS13,MP15] with almost optimal query time of Q(n)=O(n<sup>1−1/D+o(1))Q(n)=O(n<sup>{1-1/D+o(1)}) were obtained. For "fast query" structures (i.e., when Q(n)=n<sup>o(1)Q(n)=n<sup>{o(1)}), it was conjectured that a structure with space S(n)=O(n<sup>D+o(1))S(n) = O(n<sup>{D+o(1)}) is possible. The conjecture was refuted recently by Afshani and Cheng [AC21]. In the plane, they proved that S(n)=Ω(n<sup>Δ+1</sup>−o(1)/Q(n)<sup>(Δ+3)Δ/2)S(n) = \Omega(n<sup>{\Delta+1</sup> - o(1)}/Q(n)<sup>{(\Delta+3)\Delta/2}) which shows Ω(n<sup>Δ+1−o(1))\Omega(n<sup>{\Delta+1-o(1)}) space is needed for Q(n)=n<sup>o(1)Q(n) = n<sup>{o(1)}. While this refutes the conjecture, it still leaves a number of unresolved issues: the lower bound only works in 2D and for fast queries, and neither the exponent of nn or Q(n)Q(n) seem to be tight even for D=2D=2, as the current upper bound is S(n)=O(n<sup>m+o(1)/Q(n)<sup>(m−1)D/(D−1))S(n) = O(n<sup>{\boldsymbol{m}+o(1)}/Q(n)<sup>{(\boldsymbol{m}-1)D/(D-1)}) where m=(D+ΔD)−1=Ω(Δ<sup>D)\boldsymbol{m}=\binom{D+\Delta}{D}-1 = \Omega(\Delta<sup>D) is the maximum number of parameters to define a monic degree-Δ\Delta DD-variate polynomial, for any D,Δ=O(1)D,\Delta=O(1). In this paper, we resolve two of the issues: we prove a lower bound in DD-dimensions and show that when Q(n)=n<sup>o(1)+O(k)Q(n)=n<sup>{o(1)}+O(k), S(n)=Ω(n<sup>m−o(1))S(n)=\Omega(n<sup>{\boldsymbol{m}-o(1)}), which is almost tight as far as the exponent of nn is considered in the pointer machine model. When considering the exponent of Q(n)Q(n), we show that the analysis in [AC21] is tight for D=2D=2, by presenting matching upper bounds for uniform random point sets. This shows either the existing upper bounds can be improved or a new fundamentally different input set is needed to get a better lower bound.

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