On Semialgebraic Range Reporting (2203.07096v2)
Abstract: In the problem of semialgebraic range searching, we are to preprocess a set of points in $\mathbb{R}D$ such that the subset of points inside a semialgebraic region described by $O(1)$ polynomial inequalities of degree $\Delta$ 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 $Q(n)=O(n{1-1/D+o(1)})$ were obtained. For "fast query" structures (i.e., when $Q(n)=n{o(1)}$), it was conjectured that a structure with space $S(n) = O(n{D+o(1)})$ is possible. The conjecture was refuted recently by Afshani and Cheng [AC21]. In the plane, they proved that $S(n) = \Omega(n{\Delta+1 - o(1)}/Q(n){(\Delta+3)\Delta/2})$ which shows $\Omega(n{\Delta+1-o(1)})$ space is needed for $Q(n) = n{o(1)}$. 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 $n$ or $Q(n)$ seem to be tight even for $D=2$, as the current upper bound is $S(n) = O(n{\boldsymbol{m}+o(1)}/Q(n){(\boldsymbol{m}-1)D/(D-1)})$ where $\boldsymbol{m}=\binom{D+\Delta}{D}-1 = \Omega(\DeltaD)$ is the maximum number of parameters to define a monic degree-$\Delta$ $D$-variate polynomial, for any $D,\Delta=O(1)$. In this paper, we resolve two of the issues: we prove a lower bound in $D$-dimensions and show that when $Q(n)=n{o(1)}+O(k)$, $S(n)=\Omega(n{\boldsymbol{m}-o(1)})$, which is almost tight as far as the exponent of $n$ is considered in the pointer machine model. When considering the exponent of $Q(n)$, we show that the analysis in [AC21] is tight for $D=2$, 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.
Collections
Sign up for free to add this paper to one or more collections.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.