Approximating the discrete and continuous median line segments in $d$ dimensions
Ovidiu Daescu, Ka Yaw Teo

TL;DR
This paper develops algorithms to efficiently approximate median line segments in high-dimensional spaces, introducing new data structures and addressing both discrete and continuous cases with theoretical and practical implications.
Contribution
It presents novel approximation algorithms for median line segments in multiple dimensions, utilizing pair decomposition techniques and addressing the continuous case's geometric limitations.
Findings
Algorithms for $(1+\epsilon)$-approximations with polynomial time complexity
New data structures for fast distance sum approximation
Impossibility result for ruler-and-compass construction in the continuous case
Abstract
Consider a set of points in . In the discrete median line segment problem, the objective is to find a line segment bounded by a pair of points in such that the sum of the Euclidean distances from to the line segment is minimized. In the continuous median line segment problem, a real number is given, and the goal is to locate a line segment of length in such that the sum of the Euclidean distances between and the line segment is minimized. We show how to compute - and -approximations to a discrete median line segment in time and , respectively, where is the spread of line segments spanned by pairs of points. While developing our algorithms, by using the principle of pair decomposition, we derive new data structures that allow us to…
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Taxonomy
TopicsComputational Geometry and Mesh Generation · Facility Location and Emergency Management · Robotics and Sensor-Based Localization
