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Geometric dominating-set and set-cover via local-search

De, M and Lahiri, A (2023) Geometric dominating-set and set-cover via local-search. In: Computational Geometry: Theory and Applications, 113 .

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Official URL: https://doi.org/10.1016/j.comgeo.2023.102007

Abstract

In this paper, we study two classic optimization problems: minimum geometric dominating set and set cover. In the dominating-set problem, for a given set of objects in the plane as input, the objective is to choose a minimum number of input objects such that every input object is dominated by the chosen set of objects. Here, we say that one object is dominated by another if their intersection is nonempty. For the second problem, for a given set of points and objects in the plane, the objective is to choose a minimum number of objects to cover all the points. This is a particular version of the set-cover problem. Both problems have been well-studied, subject to various restrictions on the input objects. These problems are APX-hard for object sets consisting of axis-parallel rectangles, ellipses, α-fat objects of constant description complexity, and convex polygons. On the other hand, PTASs (polynomial time approximation schemes) are known for object sets consisting of disks or unit squares. Surprisingly, a PTAS was unknown even for arbitrary squares. For both problems obtaining a PTAS remains open for a large class of objects. For the dominating-set problem, we prove that a popular local-search algorithm leads to a (1+ε) approximation for a family of homothets of a convex object (which includes arbitrary squares, k-regular polygons, translated and scaled copies of a convex set, etc.) in nO(1/ε2) time. On the other hand, the same approach leads to a PTAS for the geometric covering problem when the objects are convex pseudodisks (which include disks, unit height rectangles, homothetic convex objects, etc.). Consequently, we obtain an easy-to-implement approximation algorithm for both problems for a large class of objects, significantly improving the best-known approximation guarantees. © 2023 Elsevier B.V.

Item Type: Journal Article
Publication: Computational Geometry: Theory and Applications
Publisher: Elsevier B.V.
Additional Information: The copyright for this article belongs to the Authors.
Keywords: Geometry; Local search (optimization); Polynomial approximation; Set theory, Convex objects; Dominating set problems; Dominating sets; Geometric dominating set; Geometric set cover; Geometric sets; Local search; Object sets; Polynomial time approximation schemes; Set-cover, Approximation algorithms
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 02 May 2023 09:26
Last Modified: 02 May 2023 09:26
URI: https://eprints.iisc.ac.in/id/eprint/81237

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