AN ALGORITHMIC FRAMEWORK FOR SOLVING GEOMETRIC COVERING PROBLEMS — WITH APPLICATIONS

Author:

GHASEMI TAHA12,GHASEMALIZADEH HOSSEIN1,RAZZAZI MOHAMMADREZA1

Affiliation:

1. Software Systems R&D Laboratory, Computer Engineering and IT Department, Amirkabir University of Technology, #424 Hafez Avenue, P. O. Box 15875-4413, Tehran, Iran

2. Institute for Research in Fundamental Sciences (IPM), P. O. Box 19395-5746, Tehran, Iran

Abstract

In geometric covering problems, the aim is to cover a set of given points using a minimum number of shapes with prescribed properties. These problems extend in many ways such as covering in the presence of obstacles and partial covering. Since in most cases these problems are NP-hard, researchers have developed a large number of approximation algorithms to solve them. In this paper, with a more general view on these approaches, we propose an algorithmic framework that gives an approximation algorithm for the covering problem at hand, provided that its prerequisites are satisfied. As the result, we present a general class of geometric covering problems for which one can obtain polynomial-time approximation schemes (PTASs). This class of problems involves covering point sets with bounded shapes in a space with fixed dimension. For a problem to be in this class, it must be possible to find a polynomial number of maximal shapes and it must be possible to cover all the points in a small box with a small number of shapes. Using this framework, we present two new PTASs for the problems of covering with disks in the presence of polygonal obstacles and covering with sectors (pie-shaped wedges). The first problem has never been addressed before and for the second problem, only a logarithmic approximation algorithm was known in general and a 9-approximation algorithm for wide angles.

Publisher

World Scientific Pub Co Pte Lt

Subject

Computer Science (miscellaneous)

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Target Coverage and Connectivity in Directional Wireless Sensor Networks;IEEE INFOCOM 2023 - IEEE Conference on Computer Communications;2023-05-17

2. Supervised Classification Using Feature Space Partitioning;Lecture Notes in Computer Science;2018

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