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  Fine-Grained Complexity Theory: Conditional Lower Bounds for Computational Geometry

Bringmann, K. (2021). Fine-Grained Complexity Theory: Conditional Lower Bounds for Computational Geometry. Retrieved from https://arxiv.org/abs/2110.10283.

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arXiv:2110.10283.pdf (Preprint), 184KB
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File downloaded from arXiv at 2022-01-04 12:08 Written version of a tutorial talk given at a special session of CiE'21
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 Creators:
Bringmann, Karl1, Author                 
Affiliations:
1Algorithms and Complexity, MPI for Informatics, Max Planck Society, ou_24019              

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Free keywords: Computer Science, Computational Geometry, cs.CG,Computer Science, Data Structures and Algorithms, cs.DS
 Abstract: Fine-grained complexity theory is the area of theoretical computer science
that proves conditional lower bounds based on the Strong Exponential Time
Hypothesis and similar conjectures. This area has been thriving in the last
decade, leading to conditionally best-possible algorithms for a wide variety of
problems on graphs, strings, numbers etc. This article is an introduction to
fine-grained lower bounds in computational geometry, with a focus on lower
bounds for polynomial-time problems based on the Orthogonal Vectors Hypothesis.
Specifically, we discuss conditional lower bounds for nearest neighbor search
under the Euclidean distance and Fr\'echet distance.

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Language(s): eng - English
 Dates: 2021-10-192021
 Publication Status: Published online
 Pages: 8 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: arXiv: 2110.10283
URI: https://arxiv.org/abs/2110.10283
BibTex Citekey: Bringmann2110.10283
 Degree: -

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Project name : TIPEA
Grant ID : 850979
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)

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