Combinatorial and Hodge Laplacians: Similarities and Differences

IF 10.8 1区 数学 Q1 MATHEMATICS, APPLIED SIAM Review Pub Date : 2024-08-08 DOI:10.1137/22m1482299
Emily Ribando-Gros, Rui Wang, Jiahui Chen, Yiying Tong, Guo-Wei Wei
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Abstract

SIAM Review, Volume 66, Issue 3, Page 575-601, May 2024.
As key subjects in spectral geometry and combinatorial graph theory, respectively, the (continuous) Hodge Laplacian and the combinatorial Laplacian share similarities in revealing the topological dimension and geometric shape of data and in their realization of diffusion and minimization of harmonic measures. It is believed that they also both associate with vector calculus, through the gradient, curl, and divergence, as argued in the popular usage of “Hodge Laplacians on graphs” in the literature. Nevertheless, these Laplacians are intrinsically different in their domains of definitions and applicability to specific data formats, hindering any in-depth comparison of the two approaches. For example, the spectral decomposition of a vector field on a simple point cloud using combinatorial Laplacians defined on some commonly used simplicial complexes does not give rise to the same curl-free and divergence-free components that one would obtain from the spectral decomposition of a vector field using either the continuous Hodge Laplacians defined on differential forms in manifolds or the discretized Hodge Laplacians defined on a point cloud with boundary in the Eulerian representation or on a regular mesh in the Eulerian representation. To facilitate the comparison and bridge the gap between the combinatorial Laplacian and Hodge Laplacian for the discretization of continuous manifolds with boundary, we further introduce boundary-induced graph (BIG) Laplacians using tools from discrete exterior calculus (DEC). BIG Laplacians are defined on discrete domains with appropriate boundary conditions to characterize the topology and shape of data. The similarities and differences among the combinatorial Laplacian, BIG Laplacian, and Hodge Laplacian are then examined. Through an Eulerian representation of 3D domains as level-set functions on regular grids, we show experimentally the conditions for the convergence of BIG Laplacian eigenvalues to those of the Hodge Laplacian for elementary shapes.
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组合拉普拉斯和霍奇拉普拉斯:异同
SIAM Review》,第 66 卷第 3 期,第 575-601 页,2024 年 5 月。 分别作为谱几何和组合图论的关键课题,(连续)霍奇拉普拉斯和组合拉普拉斯在揭示数据的拓扑维度和几何形状方面,以及在实现扩散和最小化调和度量方面,都有相似之处。正如文献中流行的 "图上霍奇拉普拉斯 "的用法所论证的那样,人们认为它们也都通过梯度、卷曲和发散与向量微积分相关联。然而,这些拉普拉斯在定义域和对特定数据格式的适用性方面存在本质区别,阻碍了对这两种方法的深入比较。例如,使用定义在一些常用简单复数上的组合拉普拉斯对简单点云上的矢量场进行谱分解,并不会产生与使用定义在流形微分形式上的连续霍奇拉普拉斯或使用定义在欧拉表示法中具有边界的点云上或欧拉表示法中规则网格上的离散霍奇拉普拉斯对矢量场进行谱分解时相同的无卷曲和无发散分量。为了便于比较和弥合有边界连续流形离散化的组合拉普拉斯和霍奇拉普拉斯之间的差距,我们利用离散外部微积分(DEC)的工具进一步引入了边界诱导图(BIG)拉普拉斯。BIG 拉普拉斯在离散域上定义,具有适当的边界条件,可以描述数据的拓扑和形状。然后研究了组合拉普拉斯、BIG 拉普拉斯和霍奇拉普拉斯之间的异同。通过在规则网格上将三维域表示为水平集函数的欧拉模型,我们用实验证明了基本形状的 BIG 拉普拉斯特征值向霍奇拉普拉斯特征值收敛的条件。
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来源期刊
SIAM Review
SIAM Review 数学-应用数学
CiteScore
16.90
自引率
0.00%
发文量
50
期刊介绍: Survey and Review feature papers that provide an integrative and current viewpoint on important topics in applied or computational mathematics and scientific computing. These papers aim to offer a comprehensive perspective on the subject matter. Research Spotlights publish concise research papers in applied and computational mathematics that are of interest to a wide range of readers in SIAM Review. The papers in this section present innovative ideas that are clearly explained and motivated. They stand out from regular publications in specific SIAM journals due to their accessibility and potential for widespread and long-lasting influence.
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