Time-optimal computation of the rectilinear convex hull with arbitrary orientation of sets of segments and circles

We explore an extension to rectilinear convexity of the classic problem of computing the convex hull of a set of geometric objects. Namely, we solve the problem of computing the rectilinear convex hull witharbitraryorientation for asetofsegmentsandcircles.Wedescribe efficient algorithms to compute a...

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Bibliographic Details
Authors: Alegria, Carlos, Dallant, Justin, Pérez Lantero, Pablo, Seara Ojea, Carlos|||0000-0002-0095-1725
Format: article
Publication Date:2025
Country:España
Institution:Universitat Politècnica de Catalunya (UPC)
Repository:UPCommons. Portal del coneixement obert de la UPC
Language:English
OAI Identifier:oai:upcommons.upc.edu:2117/427276
Online Access:https://hdl.handle.net/2117/427276
https://dx.doi.org/10.1007/s10898-025-01482-9
Access Level:Open access
Keyword:Rectilinear convex hull
Segments
Simple polygonal line
Circles
Theory of computation
Computational geometry
Àrees temàtiques de la UPC::Matemàtiques i estadística::Geometria::Geometria computacional
Description
Summary:We explore an extension to rectilinear convexity of the classic problem of computing the convex hull of a set of geometric objects. Namely, we solve the problem of computing the rectilinear convex hull witharbitraryorientation for asetofsegmentsandcircles.Wedescribe efficient algorithms to compute and maintain the objects appearing on the boundary of the rectilinear convex hull of such sets, while we rotate the coordinate axes by an angle that goes from 0 to 2p. We first consider a set of n segments. If the segments are not necessarily disjoint, we describe an algorithm that runs in optimal (n logn) time and O(na(n)) space, where a(n) is the extremely slowly growing inverse of Ackermann’s function. If instead the segments form a simple polygonal chain, we describe an algorithm that improves the previous space complexity to (n). We then extend the techniques used in these algorithms to a set of n circles. The resulting algorithm runs in optimal (n logn) time and (n) space.