Discriminating Codes in Geometric Setups

Authors Sanjana Dey, Florent Foucaud, Subhas C. Nandy, Arunabha Sen

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Sanjana Dey
  • ACM Unit, Indian Statistical Institute, Kolkata, India
Florent Foucaud
  • Univ. Bordeaux, Bordeaux INP, CNRS, LaBRI, UMR5800, 33400 Talence, France
Subhas C. Nandy
  • ACM Unit, Indian Statistical Institute, Kolkata, India
Arunabha Sen
  • Arizona State University, Tempe, AZ, USA

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Sanjana Dey, Florent Foucaud, Subhas C. Nandy, and Arunabha Sen. Discriminating Codes in Geometric Setups. In 31st International Symposium on Algorithms and Computation (ISAAC 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 181, pp. 24:1-24:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)


We study two geometric variations of the discriminating code problem. In the discrete version, a finite set of points P and a finite set of objects S are given in ℝ^d. The objective is to choose a subset S^* ⊆ S of minimum cardinality such that the subsets S_i^* ⊆ S^* covering p_i, satisfy S_i^* ≠ ∅ for each i = 1,2,…, n, and S_i^* ≠ S_j^* for each pair (i,j), i ≠ j. In the continuous version, the solution set S^* can be chosen freely among a (potentially infinite) class of allowed geometric objects. In the 1-dimensional case (d = 1), the points are placed on some fixed-line L, and the objects in S are finite segments of L (called intervals). We show that the discrete version of this problem is NP-complete. This is somewhat surprising as the continuous version is known to be polynomial-time solvable. This is also in contrast with most geometric covering problems, which are usually polynomial-time solvable in 1D. We then design a polynomial-time 2-approximation algorithm for the 1-dimensional discrete case. We also design a PTAS for both discrete and continuous cases when the intervals are all required to have the same length. We then study the 2-dimensional case (d = 2) for axis-parallel unit square objects. We show that both continuous and discrete versions are NP-hard, and design polynomial-time approximation algorithms with factors 4+ε and 32+ε, respectively (for every fixed ε > 0).

Subject Classification

ACM Subject Classification
  • Theory of computation → Approximation algorithms analysis
  • Discriminating code
  • Approximation algorithm
  • Segment stabbing
  • Geometric Hitting set


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