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arXiv 2509.08070math.NAcs.NA
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度量空间中的细分格式

Subdivision Schemes in Metric Spaces

School of Mathematical Sciences, Tel Aviv University(特拉维夫大学数学科学学院)

Nira Dyn, Nir Sharon

AI总结:

提出完整度量空间上非线性细分格式的统一框架,通过收缩性和两种邻近性概念证明收敛定理,并应用于紧集度量空间、Wasserstein空间等实例。

摘要:

我们在完备度量空间(CMS)上建立了非线性细分格式的统一框架。首先介绍CMS预备知识,并在CMS中形式化细化过程,保留关键结构性质(如局部性)。我们在收缩性条件下证明了一个收敛定理,并展示了其适用性。为了解决收缩性未知的格式,我们引入了两种邻近性概念。与文献中大多将非线性格式与线性格式比较不同,我们的邻近性方法将非线性格式与另一个已知收缩性的非线性格式相关联。具体而言,第一类邻近性比较两个格式在单步细化后的结果,如同经典理论,从足够稠密的初始数据出发可得到收敛性。第二类邻近性监测所有细化层级上的对齐,无需密度假设即可提供强收敛性。我们制定并证明了相应的定理,并通过各种例子加以说明,例如$\R^n$中紧集度量空间上的格式、Wasserstein空间上的格式,以及几何Hermite度量空间。这些结果将细分理论从欧几里得和流形值数据扩展到度量空间中的数据。

英文摘要:

We develop a unified framework for nonlinear subdivision schemes on complete metric spaces (CMS). We begin with CMS preliminaries and formalize refinement in CMS, retaining key structural properties, such as locality. We prove a convergence theorem under contractivity and demonstrate its applicability. To address schemes where contractivity is unknown, we introduce two notions of proximity. Our proximity methods relate a nonlinear scheme to another nonlinear scheme with known contractivity, rather than to a linear scheme, as in much of the literature. Specifically, the first type proximity compares the two schemes after a single refinement step and, as in the classical theory, yields convergence from sufficiently dense initial data. The proximity of the second type monitors alignment across all refinement levels and provides strong convergence without density assumptions. We formulate and prove the corresponding theorems, and illustrate them with various examples, such as schemes over metric spaces of compact sets in $\R^n$ and schemes over the Wasserstein space, as well as a geometric Hermite metric space. These results extend subdivision theory beyond Euclidean and manifold-valued data for data in metric spaces.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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