2020
DOI: 10.1093/imaiai/iaz033
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Minimal Lipschitz and ∞-harmonic extensions of vector-valued functions on finite graphs

Abstract: This paper deals with extensions of vector-valued functions on finite graphs fulfilling distinguished minimality properties. We show that so-called lex and L-lex minimal extensions are actually the same and call them minimal Lipschitz extensions. Then we prove that the solution of the graph p-Laplacians converge to these extensions as p → ∞. Furthermore, we examine the relation between minimal Lipschitz extensions and iterated weighted midrange filters and address their connection to ∞-Laplacians for scalar-va… Show more

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Cited by 2 publications
(2 citation statements)
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“…In our future research, we will be also interested in L 1 and L ∞ spaces. At least for finite dimensional L ∞ spaces, averaged operators were recently addressed in connection with L ∞ Laplacians in imaging in [18]. The notion of α-firmly nonexpansive operator has also been considered in metric spaces, see for instance [9,10] for the case of a CAT(0) space and [11] for a recent generalization to (non-linear) metric spaces.…”
Section: Introductionmentioning
confidence: 99%
“…In our future research, we will be also interested in L 1 and L ∞ spaces. At least for finite dimensional L ∞ spaces, averaged operators were recently addressed in connection with L ∞ Laplacians in imaging in [18]. The notion of α-firmly nonexpansive operator has also been considered in metric spaces, see for instance [9,10] for the case of a CAT(0) space and [11] for a recent generalization to (non-linear) metric spaces.…”
Section: Introductionmentioning
confidence: 99%
“…In our future research, we will be also interested in L 1 and L ∞ spaces. At least for finite dimensional L ∞ spaces, averaged operators were recently addressed in connection with L ∞ Laplacians in imaging in [16]. The notion of α-firmly nonexpansive operator has also been considered in metric spaces, see for instance [7], [8] for the case of a CAT(0) space and [9] for a recent generalization to (non-linear) metric spaces.…”
Section: Introductionmentioning
confidence: 99%