Subquadratic Algorithms for Some \textsc{3Sum}-Hard Geometric Problems in the Algebraic Decision Tree Model

TL;DR

This paper introduces subquadratic algorithms within the algebraic decision-tree model for certain 3Sum-hard geometric problems, improving computational efficiency by leveraging polynomial partitioning and order type-based point location.

Contribution

It provides the first subquadratic algebraic decision-tree algorithms for these problems, utilizing a primal-dual range searching approach and order type-based point location techniques.

Findings

Achieved $O(n^{60/31+\varepsilon})$ time complexity for the problems.

Extended polynomial partitioning methods to geometric intersection counting.

Demonstrated the effectiveness of order type-based point location in geometric algorithms.

Abstract

We present subquadratic algorithms in the algebraic decision-tree model for several \textsc{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 $\Delta\in C$, the number of intersection points between the segments of $A$ and those of $B$ that lie in $\Delta$. 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 $O((n^2/\log^2n)\log^{O(1)}\log n)$ time. We present solutions in the algebraic decision-tree model whose cost is $O(n^{60/31+\varepsilon})$, for any $\varepsilon>0$. 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 \emph{order type} of the lines, a "handicap" that turns out to be beneficial for speeding up our algorithm.