arXiv subjects

Timothy M. Chan

Publications and source records attributed to Timothy M. Chan.

At least 37 records · Page 2Linked to original sources

arXiv2024

Semialgebraic Range Stabbing, Ray Shooting, and Intersection Counting in the Plane

Polynomial partitioning techniques have recently led to improved geometric data structures for a variety of fundamental problems related to semialgebraic range searching and intersection searching in 3D and higher dimensions (e.g., see [Agarwal, Aronov, Ezra, and Zahl, SoCG 2019; Ezra and Sharir, SoCG 2021; Agarwal, Aronov, Ezra, Katz, and Sharir, SoCG 2022]). They have also led to improved algorithms for offline versions of semialgebraic range searching in 2D, via lens-cutting [Sharir and Zahl (2017)]. In this paper, we show that these techniques can yield new data structures for a number of other 2D problems even for online queries: 1. Semialgebraic range stabbing. We present a data structure for $n$ semialgebraic ranges in 2D of constant description complexity with $O(n^{3/2+\varepsilon})$ preprocessing time and space, so that we can count the number of ranges containing a query point in $O(n^{1/4+\varepsilon})$ time, for an arbitrarily small constant $\varepsilon>0$. 2. Ray shooting amid algebraic arcs. We present a data structure for $n$ algebraic arcs in 2D of constant description complexity with $O(n^{3/2+\varepsilon})$ preprocessing time and space, so that we can find the first arc hit by a query (straight-line) ray in $O(n^{1/4+\varepsilon})$ time. 3. Intersection counting amid algebraic arcs. We present a data structure for $n$ algebraic arcs in 2D of constant description complexity with $O(n^{3/2+\varepsilon})$ preprocessing time and space, so that we can count the number of intersection points with a query algebraic arc of constant description complexity in $O(n^{1/2+\varepsilon})$ time. In particular, this implies an $O(n^{3/2+\varepsilon})$-time algorithm for counting intersections between two sets of $n$ algebraic arcs in 2D.

cs.CG↗

arXiv2024

Enclosing Points with Geometric Objects

Let $X$ be a set of points in $\mathbb{R}^2$ and $\mathcal{O}$ be a set of geometric objects in $\mathbb{R}^2$, where $|X| + |\mathcal{O}| = n$. We study the problem of computing a minimum subset $\mathcal{O}^* \subseteq \mathcal{O}$ that encloses all points in $X$. Here a point $x \in X$ is enclosed by $\mathcal{O}^*$ if it lies in a bounded connected component of $\mathbb{R}^2 \backslash (\bigcup_{O \in \mathcal{O}^*} O)$. We propose two algorithmic frameworks to design polynomial-time approximation algorithms for the problem. The first framework is based on sparsification and min-cut, which results in $O(1)$-approximation algorithms for unit disks, unit squares, etc. The second framework is based on LP rounding, which results in an $O(α(n)\log n)$-approximation algorithm for segments, where $α(n)$ is the inverse Ackermann function, and an $O(\log n)$-approximation algorithm for disks.

cs.CG↗

arXiv2024

Fully Dynamic Geometric Vertex Cover and Matching

In this work, we study two fundamental graph optimization problems, minimum vertex cover (MVC) and maximum-cardinality matching (MCM), for intersection graphs of geometric objects, e.g., disks, rectangles, hypercubes, etc., in $d$-dimensional Euclidean space. We consider the problems in fully dynamic settings, allowing insertions and deletions of objects. We develop a general framework for dynamic MVC in intersection graphs, achieving sublinear amortized update time for most natural families of geometric objects. In particular, we show that - - For a dynamic collection of disks in $\mathbb{R}^2$ or hypercubes in $\mathbb{R}^d$ (for constant $d$), it is possible to maintain a $(1+\varepsilon)$-approximate vertex cover in polylog amortized update time. These results also hold in the bipartite case. - For a dynamic collection of rectangles in $\mathbb{R}^2$, it is possible to maintain a $(\frac{3}{2}+\varepsilon)$-approximate vertex cover in polylog amortized update time. Along the way, we obtain the first near-linear time static algorithms for MVC in the above two cases with the same approximation factors. Next, we turn our attention to the MCM problem. Although our MVC algorithms automatically allow us to approximate the size of the MCM in bipartite geometric intersection graphs, they do not produce a matching. We give another general framework to maintain an approximate maximum matching, and further extend the approach to handle non-bipartite intersection graphs. In particular, we show that - - For a dynamic collection of (bichromatic or monochromatic) disks in $\mathbb{R}^2$ or hypercubes in $\mathbb{R}^d$ (for constant $d$), it is possible to maintain a $(1+\varepsilon)$-approximate matching in polylog amortized update time.

cs.CG↗

arXiv2023

An Optimal Algorithm for Higher-Order Voronoi Diagrams in the Plane: The Usefulness of Nondeterminism

We present the first optimal randomized algorithm for constructing the order-$k$ Voronoi diagram of $n$ points in two dimensions. The expected running time is $O(n\log n + nk)$, which improves the previous, two-decades-old result of Ramos (SoCG'99) by a $2^{O(\log^*k)}$ factor. To obtain our result, we (i) use a recent decision-tree technique of Chan and Zheng (SODA'22) in combination with Ramos's cutting construction, to reduce the problem to verifying an order-$k$ Voronoi diagram, and (ii) solve the verification problem by a new divide-and-conquer algorithm using planar-graph separators. We also describe a deterministic algorithm for constructing the $k$-level of $n$ lines in two dimensions in $O(n\log n + nk^{1/3})$ time, and constructing the $k$-level of $n$ planes in three dimensions in $O(n\log n + nk^{3/2})$ time. These time bounds (ignoring the $n\log n$ term) match the current best upper bounds on the combinatorial complexity of the $k$-level. Previously, the same time bound in two dimensions was obtained by Chan (1999) but with randomization.

cs.CG↗

arXiv2023

Simpler Reductions from Exact Triangle

In this paper, we provide simpler reductions from Exact Triangle to two important problems in fine-grained complexity: Exact Triangle with Few Zero-Weight $4$-Cycles and All-Edges Sparse Triangle. Exact Triangle instances with few zero-weight $4$-cycles was considered by Jin and Xu [STOC 2023], who used it as an intermediate problem to show $3$SUM hardness of All-Edges Sparse Triangle with few $4$-cycles (independently obtained by Abboud, Bringmann and Fischer [STOC 2023]), which is further used to show $3$SUM hardness of a variety of problems, including $4$-Cycle Enumeration, Offline Approximate Distance Oracle, Dynamic Approximate Shortest Paths and All-Nodes Shortest Cycles. We provide a simple reduction from Exact Triangle to Exact Triangle with few zero-weight $4$-cycles. Our new reduction not only simplifies Jin and Xu's previous reduction, but also strengthens the conditional lower bounds from being under the $3$SUM hypothesis to the even more believable Exact Triangle hypothesis. As a result, all conditional lower bounds shown by Jin and Xu [STOC 2023] and by Abboud, Bringmann and Fischer [STOC 2023] using All-Edges Sparse Triangle with few $4$-cycles as an intermediate problem now also hold under the Exact Triangle hypothesis. We also provide two alternative proofs of the conditional lower bound of the All-Edges Sparse Triangle problem under the Exact Triangle hypothesis, which was originally proved by Vassilevska Williams and Xu [FOCS 2020]. Both of our new reductions are simpler, and one of them is also deterministic -- all previous reductions from Exact Triangle or 3SUM to All-Edges Sparse Triangle (including Pătraşcu's seminal work [STOC 2010]) were randomized.

cs.DS↗

arXiv2023

On the Fine-Grained Complexity of Small-Size Geometric Set Cover and Discrete $k$-Center for Small $k$

We study the time complexity of the discrete $k$-center problem and related (exact) geometric set cover problems when $k$ or the size of the cover is small. We obtain a plethora of new results: - We give the first subquadratic algorithm for rectilinear discrete 3-center in 2D, running in $\widetilde{O}(n^{3/2})$ time. - We prove a lower bound of $Ω(n^{4/3-δ})$ for rectilinear discrete 3-center in 4D, for any constant $δ>0$, under a standard hypothesis about triangle detection in sparse graphs. - Given $n$ points and $n$ weighted axis-aligned unit squares in 2D, we give the first subquadratic algorithm for finding a minimum-weight cover of the points by 3 unit squares, running in $\widetilde{O}(n^{8/5})$ time. We also prove a lower bound of $Ω(n^{3/2-δ})$ for the same problem in 2D, under the well-known APSP Hypothesis. For arbitrary axis-aligned rectangles in 2D, our upper bound is $\widetilde{O}(n^{7/4})$. - We prove a lower bound of $Ω(n^{2-δ})$ for Euclidean discrete 2-center in 13D, under the Hyperclique Hypothesis. This lower bound nearly matches the straightforward upper bound of $\widetilde{O}(n^ω)$, if the matrix multiplication exponent $ω$ is equal to 2. - We similarly prove an $Ω(n^{k-δ})$ lower bound for Euclidean discrete $k$-center in $O(k)$ dimensions for any constant $k\ge 3$, under the Hyperclique Hypothesis. This lower bound again nearly matches known upper bounds if $ω=2$. - We also prove an $Ω(n^{2-δ})$ lower bound for the problem of finding 2 boxes to cover the largest number of points, given $n$ points and $n$ boxes in 12D. This matches the straightforward near-quadratic upper bound.

cs.CG↗

arXiv2023

Constant-Hop Spanners for More Geometric Intersection Graphs, with Even Smaller Size

In SoCG 2022, Conroy and Tóth presented several constructions of sparse, low-hop spanners in geometric intersection graphs, including an $O(n\log n)$-size 3-hop spanner for $n$ disks (or fat convex objects) in the plane, and an $O(n\log^2 n)$-size 3-hop spanner for $n$ axis-aligned rectangles in the plane. Their work left open two major questions: (i) can the size be made closer to linear by allowing larger constant stretch? and (ii) can near-linear size be achieved for more general classes of intersection graphs? We address both questions simultaneously, by presenting new constructions of constant-hop spanners that have almost linear size and that hold for a much larger class of intersection graphs. More precisely, we prove the existence of an $O(1)$-hop spanner for arbitrary string graphs with $O(nα_k(n))$ size for any constant $k$, where $α_k(n)$ denotes the $k$-th function in the inverse Ackermann hierarchy. We similarly prove the existence of an $O(1)$-hop spanner for intersection graphs of $d$-dimensional fat objects with $O(nα_k(n))$ size for any constant $k$ and $d$. We also improve on some of Conroy and Tóth's specific previous results, in either the number of hops or the size: we describe an $O(n\log n)$-size 2-hop spanner for disks (or more generally objects with linear union complexity) in the plane, and an $O(n\log n)$-size 3-hop spanner for axis-aligned rectangles in the plane. Our proofs are all simple, using separator theorems, recursion, shifted quadtrees, and shallow cuttings.

cs.CG↗

arXiv2023

Fredman's Trick Meets Dominance Product: Fine-Grained Complexity of Unweighted APSP, 3SUM Counting, and More

In this paper we carefully combine Fredman's trick [SICOMP'76] and Matoušek's approach for dominance product [IPL'91] to obtain powerful results in fine-grained complexity: - Under the hypothesis that APSP for undirected graphs with edge weights in $\{1, 2, \ldots, n\}$ requires $n^{3-o(1)}$ time (when $ω=2$), we show a variety of conditional lower bounds, including an $n^{7/3-o(1)}$ lower bound for unweighted directed APSP and an $n^{2.2-o(1)}$ lower bound for computing the Minimum Witness Product between two $n \times n$ Boolean matrices, even if $ω=2$, improving upon their trivial $n^2$ lower bounds. Our techniques can also be used to reduce the unweighted directed APSP problem to other problems. In particular, we show that (when $ω= 2$), if unweighted directed APSP requires $n^{2.5-o(1)}$ time, then Minimum Witness Product requires $n^{7/3-o(1)}$ time. - We show that, surprisingly, many central problems in fine-grained complexity are equivalent to their natural counting versions. In particular, we show that Min-Plus Product and Exact Triangle are subcubically equivalent to their counting versions, and 3SUM is subquadratically equivalent to its counting version. - We obtain new algorithms using new variants of the Balog-Szemerédi-Gowers theorem from additive combinatorics. For example, we get an $O(n^{3.83})$ time deterministic algorithm for exactly counting the number of shortest paths in an arbitrary weighted graph, improving the textbook $\widetilde{O}(n^{4})$ time algorithm. We also get faster algorithms for 3SUM in preprocessed universes, and deterministic algorithms for 3SUM on monotone sets in $\{1, 2, \ldots, n\}^d$.

cs.DS↗

arXiv2023

Minimum $L_\infty$ Hausdorff Distance of Point Sets Under Translation: Generalizing Klee's Measure Problem

We present a (combinatorial) algorithm with running time close to $O(n^d)$ for computing the minimum directed $L_\infty$ Hausdorff distance between two sets of $n$ points under translations in any constant dimension $d$. This substantially improves the best previous time bound near $O(n^{5d/4})$ by Chew, Dor, Efrat, and Kedem from more than twenty years ago. Our solution is obtained by a new generalization of Chan's algorithm [FOCS'13] for Klee's measure problem. To complement this algorithmic result, we also prove a nearly matching conditional lower bound close to $Ω(n^d)$ for combinatorial algorithms, under the Combinatorial $k$-Clique Hypothesis.

cs.CG↗

arXiv2023

On the Number of Incidences When Avoiding an Induced Biclique in Geometric Settings

Given a set of points $P$ and a set of regions $\mathcal{O}$, an incidence is a pair $(p,o ) \in P \times \mathcal{O}$ such that $p \in o$. We obtain a number of new results on a classical question in combinatorial geometry: What is the number of incidences (under certain restrictive conditions)? We prove a bound of $O\bigl( k n(\log n/\log\log n)^{d-1} \bigr)$ on the number of incidences between $n$ points and $n$ axis-parallel boxes in $\mathbb{R}^d$, if no $k$ boxes contain $k$ common points, that is, if the incidence graph between the points and the boxes does not contain $K_{k,k}$ as a subgraph. This new bound improves over previous work, by Basit, Chernikov, Starchenko, Tao, and Tran (2021), by more than a factor of $\log^d n$ for $d >2$. Furthermore, it matches a lower bound implied by the work of Chazelle (1990), for $k=2$, thus settling the question for points and boxes. We also study several other variants of the problem. For halfspaces, using shallow cuttings, we get a linear bound in two and three dimensions. We also present linear (or near linear) bounds for shapes with low union complexity, such as pseudodisks and fat triangles.

cs.CG↗

arXiv2022

Finding Triangles and Other Small Subgraphs in Geometric Intersection Graphs

We consider problems related to finding short cycles, small cliques, small independent sets, and small subgraphs in geometric intersection graphs. We obtain a plethora of new results. For example: * For the intersection graph of $n$ line segments in the plane, we give algorithms to find a 3-cycle in $O(n^{1.408})$ time, a size-3 independent set in $O(n^{1.652})$ time, a 4-clique in near-$O(n^{24/13})$ time, and a $k$-clique (or any $k$-vertex induced subgraph) in $O(n^{0.565k+O(1)})$ time for any constant $k$; we can also compute the girth in near-$O(n^{3/2})$ time. * For the intersection graph of $n$ axis-aligned boxes in a constant dimension $d$, we give algorithms to find a 3-cycle in $O(n^{1.408})$ time for any $d$, a 4-clique (or any 4-vertex induced subgraph) in $O(n^{1.715})$ time for any $d$, a size-4 independent set in near-$O(n^{3/2})$ time for any $d$, a size-5 independent set in near-$O(n^{4/3})$ time for $d=2$, and a $k$-clique (or any $k$-vertex induced subgraph) in $O(n^{0.429k+O(1)})$ time for any $d$ and any constant $k$. * For the intersection graph of $n$ fat objects in any constant dimension $d$, we give an algorithm to find any $k$-vertex (non-induced) subgraph in $O(n\log n)$ time for any constant $k$, generalizing a result by Kaplan, Klost, Mulzer, Roddity, Seiferth, and Sharir (1999) for 3-cycles in 2D disk graphs. A variety of techniques is used, including geometric range searching, biclique covers, "high-low" tricks, graph degeneracy and separators, and shifted quadtrees. We also prove a near-$Ω(n^{4/3})$ conditional lower bound for finding a size-4 independent set for boxes.

cs.CG↗

arXiv2022

Simplex Range Searching Revisited: How to Shave Logs in Multi-Level Data Structures

We revisit the classic problem of simplex range searching and related problems in computational geometry. We present a collection of new results which improve previous bounds by multiple logarithmic factors that were caused by the use of multi-level data structures. Highlights include the following: $\bullet$ For a set of $n$ points in a constant dimension $d$, we give data structures with $O(n^d)$ (or slightly better) space that can answer simplex range counting queries in optimal $O(\log n)$ time and simplex range reporting queries in optimal $O(\log n + k)$ time, where $k$ denotes the output size. For semigroup range searching, we obtain $O(\log n)$ query time with $O(n^d\mathop{\rm polylog}n)$ space. Previous data structures with similar space bounds by Matoušek from nearly three decades ago had $O(\log^{d+1}n)$ or $O(\log^{d+1}n + k)$ query time. $\bullet$ For a set of $n$ simplices in a constant dimension $d$, we give data structures with $O(n)$ space that can answer stabbing counting queries (counting the number of simplices containing a query point) in $O(n^{1-1/d})$ time, and stabbing reporting queries in $O(n^{1-1/d}+k)$ time. Previous data structures had extra $\log^d n$ factors in space and query time. $\bullet$ For a set of $n$ (possibly intersecting) line segments in 2D, we give a data structure with $O(n)$ space that can answer ray shooting queries in $O(\sqrt{n})$ time. This improves Wang's recent data structure [SoCG'20] with $O(n\log n)$ space and $O(\sqrt{n}\log n)$ query time.

cs.CG↗

arXiv2022

Hopcroft's Problem, Log-Star Shaving, 2D Fractional Cascading, and Decision Trees

We revisit Hopcroft's problem and related fundamental problems about geometric range searching. Given $n$ points and $n$ lines in the plane, we show how to count the number of point-line incidence pairs or the number of point-above-line pairs in $O(n^{4/3})$ time, which matches the conjectured lower bound and improves the best previous time bound of $n^{4/3}2^{O(\log^*n)}$ obtained almost 30 years ago by Matoušek. We describe two interesting and different ways to achieve the result: the first is randomized and uses a new 2D version of fractional cascading for arrangements of lines; the second is deterministic and uses decision trees in a manner inspired by the sorting technique of Fredman (1976). The second approach extends to any constant dimension. Many consequences follow from these new ideas: for example, we obtain an $O(n^{4/3})$-time algorithm for line segment intersection counting in the plane, $O(n^{4/3})$-time randomized algorithms for bichromatic closest pair and Euclidean minimum spanning tree in three or four dimensions, and a randomized data structure for halfplane range counting in the plane with $O(n^{4/3})$ preprocessing time and space and $O(n^{1/3})$ query time.

cs.CG↗

arXiv2022

Hardness for Triangle Problems under Even More Believable Hypotheses: Reductions from Real APSP, Real 3SUM, and OV

The $3$SUM hypothesis, the APSP hypothesis and SETH are the three main hypotheses in fine-grained complexity. So far, within the area, the first two hypotheses have mainly been about integer inputs in the Word RAM model of computation. The "Real APSP" and "Real $3$SUM" hypotheses, which assert that the APSP and $3$SUM hypotheses hold for real-valued inputs in a reasonable version of the Real RAM model, are even more believable than their integer counterparts. Under the very believable hypothesis that at least one of the Integer $3$SUM hypothesis, Integer APSP hypothesis or SETH is true, Abboud, Vassilevska W. and Yu [STOC 2015] showed that a problem called Triangle Collection requires $n^{3-o(1)}$ time on an $n$-node graph. Our main result is a nontrivial lower bound for a slight generalization of Triangle Collection, called All-Color-Pairs Triangle Collection, under the even more believable hypothesis that at least one of the Real $3$SUM, the Real APSP, and the OV hypotheses is true. Combined with slight modifications of prior reductions, we obtain polynomial conditional lower bounds for problems such as the (static) ST-Max Flow problem and dynamic Max Flow, now under the new weaker hypothesis. Our main result is built on the following two lines of reductions. * Real APSP and Real $3$SUM hardness for the All-Edges Sparse Triangle problem. Prior reductions only worked from the integer variants of these problems. * Real APSP and OV hardness for a variant of the Boolean Matrix Multiplication problem. Along the way we show that Triangle Collection is equivalent to a simpler restricted version of the problem, simplifying prior work. Our techniques also have other interesting implications, such as a super-linear lower bound of Integer All-Numbers $3$SUM based on the Real $3$SUM hypothesis, and a tight lower bound for a string matching problem based on the OV hypothesis.

cs.CC↗

arXiv2021

Optimal Algorithms for Geometric Centers and Depth

$\renewcommand{\Re}{\mathbb{R}}$ We develop a general randomized technique for solving "implic it" linear programming problems, where the collection of constraints are defined implicitly by an underlying ground set of elements. In many cases, the structure of the implicitly defined constraints can be exploited in order to obtain efficient linear program solvers. We apply this technique to obtain near-optimal algorithms for a variety of fundamental problems in geometry. For a given point set $P$ of size $n$ in $\Re^d$, we develop algorithms for computing geometric centers of a point set, including the centerpoint and the Tukey median, and several other more involved measures of centrality. For $d=2$, the new algorithms run in $O(n\log n)$ expected time, which is optimal, and for higher constant $d>2$, the expected time bound is within one logarithmic factor of $O(n^{d-1})$, which is also likely near optimal for some of the problems.

cs.CG↗

arXiv2021

Dynamic Geometric Set Cover, Revisited

Geometric set cover is a classical problem in computational geometry, which has been extensively studied in the past. In the dynamic version of the problem, points and ranges may be inserted and deleted, and our goal is to efficiently maintain a set cover solution (satisfying certain quality requirement). In this paper, we give a plethora of new dynamic geometric set cover data structures in 1D and 2D, which significantly improve and extend the previous results: 1. The first data structure for $(1+\varepsilon)$-approximate dynamic interval set cover with polylogarithmic amortized update time. Specifically, we achieve an update time of $O(\log^3 n/\varepsilon)$, improving the $O(n^δ/\varepsilon)$ bound of Agarwal et al. [SoCG'20], where $δ>0$ denotes an arbitrarily small constant. 2. A data structure for $O(1)$-approximate dynamic unit-square set cover with $2^{O(\sqrt{\log n})}$ amortized update time, substantially improving the $O(n^{1/2+δ})$ update time of Agarwal et al. [SoCG'20]. 3. A data structure for $O(1)$-approximate dynamic square set cover with $O(n^{1/2+δ})$ randomized amortized update time, improving the $O(n^{2/3+δ})$ update time of Chan and He [SoCG'21]. 4. A data structure for $O(1)$-approximate dynamic 2D halfplane set cover with $O(n^{17/23+δ})$ randomized amortized update time. The previous solution for halfplane set cover by Chan and He [SoCG'21] is slower and can only report the size of the approximate solution. 5. The first sublinear results for the \textit{weighted} version of dynamic geometric set cover. Specifically, we give a data structure for $(3+o(1))$-approximate dynamic weighted interval set cover with $2^{O(\sqrt{\log n \log\log n})}$ amortized update time and a data structure for $O(1)$-approximate dynamic weighted unit-square set cover with $O(n^δ)$ amortized update time.

cs.CG↗

arXiv2021

More Dynamic Data Structures for Geometric Set Cover with Sublinear Update Time

We study geometric set cover problems in dynamic settings, allowing insertions and deletions of points and objects. We present the first dynamic data structure that can maintain an $O(1)$-approximation in sublinear update time for set cover for axis-aligned squares in 2D. More precisely, we obtain randomized update time $O(n^{2/3+δ})$ for an arbitrarily small constant $δ>0$. Previously, a dynamic geometric set cover data structure with sublinear update time was known only for unit squares by Agarwal, Chang, Suri, Xiao, and Xue [SoCG 2020]. If only an approximate size of the solution is needed, then we can also obtain sublinear amortized update time for disks in 2D and halfspaces in 3D. As a byproduct, our techniques for dynamic set cover also yield an optimal randomized $O(n\log n)$-time algorithm for static set cover for 2D disks and 3D halfspaces, improving our earlier $O(n\log n(\log\log n)^{O(1)})$ result [SoCG 2020].

cs.CG↗