Approximating the Generalized Minimum Manhattan Network Problem

Aparna Das, Krzysztof Fleszar, Stephen Kobourov, Joachim Spoerhase, Sankar Veeramoni, Alexander Wolff

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


We consider the generalized minimum Manhattan network problem (GMMN). The input to this problem is a set R of n pairs of terminals, which are points in R2. The goal is to find a minimum-length rectilinear network that connects every pair in R by a Manhattan path, that is, a path of axis-parallel line segments whose total length equals the pair’s Manhattan distance. This problem is a natural generalization of the extensively studied minimum Manhattan network problem (MMN) in which R consists of all possible pairs of terminals. Another important special case is the well-known rectilinear Steiner arborescence problem (RSA). As a generalization of these problems, GMMN is NP-hard. No approximation algorithms are known for general GMMN. We obtain an O(log n) -approximation algorithm for GMMN. Our solution is based on a stabbing technique, a novel way of attacking Manhattan network problems. Some parts of our algorithm generalize to higher dimensions, yielding a simple O(log d + 1n) -approximation algorithm for the problem in arbitrary fixed dimension d. As a corollary, we obtain an exponential improvement upon the previously best O(nε) -ratio for MMN in d dimensions (ESA 2011). En route, we show that an existing O(log n) -approximation algorithm for 2D-RSA generalizes to higher dimensions.

Original languageEnglish (US)
Pages (from-to)1170-1190
Number of pages21
Issue number4
StatePublished - Apr 1 2018


  • Approximation algorithms
  • Computational geometry
  • Minimum Manhattan Network

ASJC Scopus subject areas

  • Computer Science(all)
  • Computer Science Applications
  • Applied Mathematics


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