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On Computing the k-Shortcut Fréchet Distance

Published: 05 August 2024 Publication History

Abstract

The Fréchet distance is a popular measure of dissimilarity for polygonal curves. It is defined as a min–max formulation that considers all orientation-preserving bijective mappings between the two curves. Because of its susceptibility to noise, Driemel and Har-Peled introduced the shortcut Fréchet distance in 2012, where one is allowed to take shortcuts along one of the curves, similar to the edit distance for sequences. We analyse the parameterised version of this problem, where the number of shortcuts is bounded by a parameter \(k\) . The corresponding decision problem can be stated as follows: Given two polygonal curves \(T\) and \(B\) of at most \(n\) vertices, a parameter \(k\) and a distance threshold \(\delta\) , is it possible to introduce \(k\) shortcuts along \(B\) such that the Fréchet distance of the resulting curve and the curve \(T\) is at most \(\delta\) ? We study this problem for polygonal curves in the plane. We provide a complexity analysis for this problem with the following results: (1) there exists a decision algorithm with running time in \(\mathcal{O}(kn^{2k+2}\log n)\) ; (2) assuming the exponential-time hypothesis (ETH), there exists no algorithm with running time bounded by \(n^{o(k)}\) . In contrast, we also show that efficient approximate decider algorithms are possible, even when \(k\) is large. We present a \((3+\varepsilon)\) -approximate decider algorithm with running time in \(\mathcal{O}(kn^{2}\log^{2}n)\) for fixed \(\varepsilon\) . In addition, we can show that, if \(k\) is a constant and the two curves are \(c\) -packed for some constant \(c\) , then the approximate decider algorithm runs in near-linear time.

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Published In

cover image ACM Transactions on Algorithms
ACM Transactions on Algorithms  Volume 20, Issue 4
October 2024
416 pages
EISSN:1549-6333
DOI:10.1145/3613685
  • Editor:
  • Edith Cohen
Issue’s Table of Contents

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 August 2024
Online AM: 23 May 2024
Accepted: 30 April 2024
Revised: 17 January 2024
Received: 08 November 2022
Published in TALG Volume 20, Issue 4

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Author Tags

  1. Fréchet distance
  2. partial similarity
  3. conditional lower bounds
  4. approximation algorithms

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  • Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)

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