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Polyline Simplification has Cubic Complexity

Authors Karl Bringmann, Bhaskar Ray Chaudhury

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Karl Bringmann
  • Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany
Bhaskar Ray Chaudhury
  • Max Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany
  • Graduate School of Computer Science Saarbrücken, Saarland Informatics Campus, Germany

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Karl Bringmann and Bhaskar Ray Chaudhury. Polyline Simplification has Cubic Complexity. In 35th International Symposium on Computational Geometry (SoCG 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 129, pp. 18:1-18:16, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2019)


In the classic polyline simplification problem we want to replace a given polygonal curve P, consisting of n vertices, by a subsequence P' of k vertices from P such that the polygonal curves P and P' are "close". Closeness is usually measured using the Hausdorff or Fréchet distance. These distance measures can be applied globally, i.e., to the whole curves P and P', or locally, i.e., to each simplified subcurve and the line segment that it was replaced with separately (and then taking the maximum). We provide an O(n^3) time algorithm for simplification under Global-Fréchet distance, improving the previous best algorithm by a factor of Omega(kn^2). We also provide evidence that in high dimensions cubic time is essentially optimal for all three problems (Local-Hausdorff, Local-Fréchet, and Global-Fréchet). Specifically, improving the cubic time to O(n^{3-epsilon} poly(d)) for polyline simplification over (R^d,L_p) for p = 1 would violate plausible conjectures. We obtain similar results for all p in [1,infty), p != 2. In total, in high dimensions and over general L_p-norms we resolve the complexity of polyline simplification with respect to Local-Hausdorff, Local-Fréchet, and Global-Fréchet, by providing new algorithms and conditional lower bounds.

Subject Classification

ACM Subject Classification
  • Theory of computation
  • Theory of computation → Design and analysis of algorithms
  • Theory of computation → Mathematical optimization
  • Polyline simplification
  • Fréchet distance
  • Hausdorff distance
  • Conditional lower bounds


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