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SIAM Journal on Computing
Article . 2013 . Peer-reviewed
Data sources: Crossref
DBLP
Article . 2021
Data sources: DBLP
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Computing Correlation between Piecewise-Linear Functions

Authors: Pankaj K. Agarwal; Boris Aronov; Marc J. van Kreveld; Maarten Löffler; Rodrigo I. Silveira;

Computing Correlation between Piecewise-Linear Functions

Abstract

We study the problem of computing correlation between two piecewise-linear bivariate functions defined over a common domain, where the surfaces they define in three dimensions---polyhedral terrains---can be transformed vertically by a linear transformation of the third coordinate (scaling and translation). We present a randomized algorithm that minimizes the maximum vertical distance between the graphs of the two functions, over all linear transformations of one of the terrains, in $O(n^{4/3}\operatorname{polylog}n)$ expected time, where $n$ is the total number of vertices in the graphs of the two functions. We also present approximation algorithms for minimizing the mean distance between the graphs of univariate and bivariate functions. For univariate functions we present a $(1+\varepsilon)$-approximation algorithm that runs in $O(n (1 + \log^2 (1/\varepsilon)))$ expected time for any fixed $\varepsilon >0$. The $(1+\varepsilon)$-approximation algorithm for bivariate functions runs in $O(n/\varepsilon)$ time, for any fixed $\varepsilon >0$, provided the two functions are defined over the same triangulation of their domain.

Peer Reviewed

Countries
Netherlands, Spain
Keywords

CG, 68Q25, GIS, Computational geometry, Geometria computacional, 68U05, 68W25, TIN, piecewise-linear function, correlation, Àrees temàtiques de la UPC::Matemàtiques i estadística::Geometria::Geometria computacional, :Matemàtiques i estadística::Geometria::Geometria computacional [Àrees temàtiques de la UPC], polyhedral terrain, similarity, approximation algorithm

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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