Gaussian upper bounds, volume doubling and Sobolev inequalities on graphs

Fuente: arXiv
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Main Authors: Keller, Matthias, Rose, Christian
Format: Preprint
Published: 2024
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author Keller, Matthias
Rose, Christian
author_facet Keller, Matthias
Rose, Christian
contents We investigate the equivalence of Sobolev inequalities and the conjunction of Gaussian upper heat kernel bounds and volume doubling on large scales on graphs. For the normalizing measure, we obtain their equivalence up to constants by imposing comparability of small balls and the vertex degree at their centers. If arbitrary measures are considered, we incorporate a new local regularity condition. Furthermore, new correction functions for the Gaussian, doubling, and Sobolev dimension are introduced. For the Gaussian and doubling, the variable correction functions always tend to one at infinity. Moreover, the variable Sobolev dimension can be related to the doubling dimension and the vertex degree growth.
format Preprint
id arxiv_https___arxiv_org_abs_2406_19879
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Gaussian upper bounds, volume doubling and Sobolev inequalities on graphs
Keller, Matthias
Rose, Christian
Analysis of PDEs
Probability
We investigate the equivalence of Sobolev inequalities and the conjunction of Gaussian upper heat kernel bounds and volume doubling on large scales on graphs. For the normalizing measure, we obtain their equivalence up to constants by imposing comparability of small balls and the vertex degree at their centers. If arbitrary measures are considered, we incorporate a new local regularity condition. Furthermore, new correction functions for the Gaussian, doubling, and Sobolev dimension are introduced. For the Gaussian and doubling, the variable correction functions always tend to one at infinity. Moreover, the variable Sobolev dimension can be related to the doubling dimension and the vertex degree growth.
title Gaussian upper bounds, volume doubling and Sobolev inequalities on graphs
topic Analysis of PDEs
Probability
url https://arxiv.org/abs/2406.19879