[[EQUATION]]-图上的拉普拉斯进化问题的连续统极限:[[EQUATION]]图与稀疏图

Imad El Bouchairi, J. Fadili, Abderrahim El Moataz
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引用次数: 0

摘要

本文研究了具有齐次Neumann边界条件的稀疏图上的离散化[[EQUATION]] -拉普拉斯演化问题的连续极限。通过处理更一般的核类(可能是奇异类)和图序列(其极限是所谓的[[EQUATION]] -图子),这远远超出了已知的结果。更准确地说,我们导出了由两个不同演化系统(即具有不同核、第二成员和初始数据)定义的两个连续时间轨迹之间的距离界限。同样地,我们给出了一个边界,在这种情况下,一个轨迹是离散的,另一个是连续的。反过来,这些结果使我们建立了稀疏随机图上的[[EQUATION]] -拉普拉斯问题的完全离散化的误差估计。特别地,我们提供了离散模型的解随着顶点数的增加而收敛到连续问题的解的速率。
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Continuum limit of [[EQUATION]]-Laplacian evolution problems on graphs: [[EQUATION]]graphons and sparse graphs
In this paper we study continuum limits of the discretized [[EQUATION]] -Laplacian evolution problem on sparse graphs with homogeneous Neumann boundary conditions. This goes far beyond known results by handling much more general class of kernels, possibly singular, and graph sequences whose limit are the so-called [[EQUATION]] -graphons. More precisely, we derive a bound on the distance between two continuous-in-time trajectories defined by two different evolution systems (i.e. with different kernels, second member and initial data). Similarly, we provide a bound in the case that one of the trajectories is discrete-in-time and the other is continuous. In turn, these results lead us to establish error estimates of the full discretization of the [[EQUATION]] -Laplacian problem on sparse random graphs. In particular, we provide rate of convergence of solutions for the discrete models to the solution of the continuous problem as the number of vertices grows.
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来源期刊
CiteScore
2.70
自引率
5.30%
发文量
27
审稿时长
6-12 weeks
期刊介绍: M2AN publishes original research papers of high scientific quality in two areas: Mathematical Modelling, and Numerical Analysis. Mathematical Modelling comprises the development and study of a mathematical formulation of a problem. Numerical Analysis comprises the formulation and study of a numerical approximation or solution approach to a mathematically formulated problem. Papers should be of interest to researchers and practitioners that value both rigorous theoretical analysis and solid evidence of computational relevance.
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