PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
May 29, 20260 citationsOpen Access

Fréchet Distance in the Imbalanced Case

LBLotte Blank

Key Points

  • The aim is to investigate the approximation limits of the discrete and continuous Fréchet distance for polygonal curves.
  • Analyzed discrete Fréchet distance in 1D and extended findings to 2D cases using polygonal curves.
  • Provided an approximation algorithm for the discrete Fréchet distance with optimal factor.
  • Developed a (3+ε)-approximation algorithm for curves in any dimension using L_p space metrics.
  • Demonstrated that the discrete Fréchet distance for 1D cannot be approximated within a factor of 2-ε in O((nm)^{1-δ}) time.
  • Increased the approximation factor to 1+√2-ε for curves in Euclidean space and 3-ε for L_∞-space.
  • Achieved a (3+ε)-approximation algorithm with a running time of O((n+m²)log n).

Abstract

Given two polygonal curves P and Q defined by n and m vertices with m ≤ n, we show that the discrete Fréchet distance in 1D cannot be approximated within a factor of 2-ε in 𝒪 ( (nm) ^1-δ) time for any ε, δ > 0 unless OVH fails. Using a similar construction, we extend this bound for curves in 2D under the continuous or discrete Fréchet distance and increase the approximation factor to 1+√2-ε (resp. 3-ε) if the curves lie in the Euclidean space (resp. in the L_∞-space). This strengthens the lower bound by Buchin, Ophelders, and Speckmann to the case where m = n^α for α ∈ (0, 1) and increases the approximation factor of 1. 001 by Bringmann. For the discrete Fréchet distance in 1D, we provide an approximation algorithm with optimal approximation factor and almost optimal running time. Further, for curves in any dimension embedded in any Lₚ space, we present a (3+ε) -approximation algorithm for the continuous and discrete Fréchet distance using 𝒪 ( (n+m²) log n) time, which almost matches the approximation factor of the lower bound for the L_∞ metric.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Lotte Blank (2026) studied this question.

synapsesocial.com/papers/6a192d2dfab5b468c4415f4ahttps://doi.org/10.4230/lipics.socg.2026.17
Ask AI
Helpful
Bookmark
Share
View Full Paper