Papers for

geometric software developers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Efficient algorithms find smallest squares covering segment endpoints

Endpoint Covering of Axis-Parallel Segments:Bichromatic and Monochromatic One-Center

Abstract: We study exact one-center optimization for axis-parallel segments using axis-parallel squares under endpoint-based coverage, where a segment is \emph{$1$-covered} if the square contains at least one of its endpoints. In the monochromatic problem, we seek a minimum-side-length square that $1$-covers all $n$ input segments. We obtain $O(n\log n)$-time algorithms for both unrestricted and segment-constrained centers. The unrestricted bound matches the known bound implied by the two-representative color-spanning-square problem, whereas the segment-constrained result is new. We also prove matching $Ω(n\log n)$ lower bounds for both center models in the fixed-order algebraic decision-tree model. In the bichromatic problem, an admissible square must fully contain all $m$ blue segments, minimize the number of red segments with an endpoint in its interior, and, subject to this minimum, maximize its side length within a prescribed bounding box. We give deterministic $O(m+n\log^2 n)$-time and $O(m+mn\log n)$-time algorithms for unrestricted and blue-segment-constrained centers, respectively.

Thu 24 SeptComputational Geometry
The gist
This paper looks at how to cover lines that run only up-down or left-right on a grid with the smallest possible square that touches their ends. The authors found fast ways to do this both when the square can be placed anywhere and when it must be placed along the lines themselves. They also studied a more complicated case where one color of lines must fit inside the square while minimizing how many lines of another color are touched. Their results include both algorithms and proofs showing that their approach is as fast as theoretically possible.
Open → 2609.29821v1

Efficient algorithms compute distances and diameter on hyperbolic surfaces

The Hyperbolic Surface Distance, Diameter, and Dirichlet Problems

Abstract: Despite the prominence of hyperbolic surfaces in mathematics, basic algorithmic questions about them, even computing the distance between two points, have remained open, leaving many features of these surfaces inaccessible. The classical machinery assumes a polyhedral structure absent on a smooth surface. We remove these obstacles. We begin with an efficient $O(g^2)$ algorithm for the distance between two points, where $g$ is the genus of the surface. Building on it, we obtain an $O(g^2 \log g)$ method for answering distance queries from a fixed source and, as a consequence, for recentering a Dirichlet domain around an arbitrary point. This understanding of distances on the surface then lets us approximate the diameter to within any $\eps$ in time $O(g^3 \log g / \eps^2)$. We further show that the diameter, a single real number encoding a great deal about the surface, is exactly computable. Its hyperbolic cosine is an algebraic number over the field encoding the coefficients of the hyperbolic isometries defining the surface.

Wed 9 SeptComputational Geometry
The gist
Measuring distances on curved surfaces called hyperbolic surfaces is a basic problem that was not solved efficiently before. The authors designed new algorithms that quickly find the shortest distance between two points on these surfaces, even when the surface is complex. They also created methods to answer many distance queries from one point and to approximate or exactly calculate the surface's diameter. Their work helps reveal important geometric features of these curved surfaces, which were hard to analyze before due to mathematical challenges.
Open → 2609.09549v1