Results 11 to 20 of about 36,100 (302)
On Coron's problem for the p-Laplacian [PDF]
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Mercuri C., Sciunzi B., Squassina M.
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On principal frequencies and inradius in convex sets [PDF]
We generalize to the case of the p-Laplacian an old result by Hersch and Protter. Namely, we show that it is possible to estimate from below the first eigenvalue of the Dirichlet p-Laplacian of a convex set in terms of its inradius. We also prove a lower
Lorenzo Brasco
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Some Liouville Theorems for the p-Laplacian
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BIRINDELLI, Isabella, F. DEMENGEL
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Blow-up in a p-Laplacian mutualistic model based on graphs
In this paper, we study a $ p\, $-Laplacian ($ p > 2 $) reaction-diffusion system based on weighted graphs that is used to describe a network mutualistic model of population ecology.
Ling Zhou, Zuhan Liu
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Notes on aplications of the dual fountain theorem to local and nonlocal elliptic equations with variable exponent [PDF]
Using the Dual Fountain Theorem we obtain some existence of infinitely many solutions for local and nonlocal elliptic equations with variable exponent. Our results correct some of the errors that have appeared recently in the literature.
Robert Stegliński
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On the eigenvectors of p-Laplacian [PDF]
Spectral analysis approaches have been actively studied in machine learning and data mining areas, due to their generality, efficiency, and rich theoretical foundations. As a natural non-linear generalization of Graph Laplacian, p-Laplacian has recently been proposed, which interpolates between a relaxation of normalized cut and the Cheeger cut ...
Dijun Luo +3 more
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Unravelling how the human brain structure gives rise to function is a central question in neuroscience and remains partially answered. Recent studies show that the graph Laplacian of the human brain’s structural connectivity (SC) plays a dominant role in
Jichao Ma +3 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yin Xi Huang +2 more
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$p$-Laplacian regularization, rooted in graph and image signal processing, introduces a parameter $p$ to control the regularization effect on these data. Smaller values of $p$ promote sparsity and interpretability, while larger values encourage smoother solutions.
Nguyen, Tuan +3 more
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