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arXiv 2602.22784math.AT
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这一切都关于覆盖:覆盖细化的持续同调

It's All About Covers: Persistent Homology of Cover Refinements

Scuola Normale Superiore(比萨高等师范学院);Inria-Saclay(法国国家信息与自动化研究所萨克雷中心)

António Leitão

AI总结:

本文提出通过覆盖关系构建持续同调框架,简化了覆盖细化过程,实现更高效的同调计算,为 Vietoris-Rips 过滤提供更小的鲁棒近似。

摘要:

持续同调的计算成本常由底层单纯形过滤的生长主导。许多不同的过滤存在,各有假设和权衡,但都面临某种形式的生长,最坏情况下可能呈指数级增长,如 Vietoris-Rips。我们重新将此问题置于覆盖层面,开发一个框架,通过简单的覆盖关系构建、分析和比较过滤和持续模块,而不是在单纯复形层面。保证通过任何保持细化映射连续性的函子传播,我们给出两个此类函子的例子:Nerve 和 Co-Nerve。在这一层面工作大大简化,带来更强、更一般的结论。我们探索这一视角,并展示如何用它构建一个鲁棒近似 Vietoris-Rips 过滤,大小小得多,同时在任意度量空间中保持 log^3 交织。所得到的过滤恢复数据点数量的近线性缩放,并使我们能够高效地在高阶捕捉同调。

英文摘要:

The computational cost of persistent homology is often dominated by the growth of the underlying simplicial filtrations. Many different filtrations exist, each with its own assumptions and trade-offs, but all face some form of this growth which can be exponential in the worst case, as for the Vietoris-Rips. We recast this problem at the level of covers, developing a framework in which filtrations and persistence modules can be constructed, analyzed, and compared through simple relations between covers rather than at the level of simplicial complexes. The guarantees propagate through any functor that preserves the contiguity of refinement maps, we give the example of two such functors: the Nerve and the Co-Nerve. Working at this level is drastically simpler, with stronger, more general consequences. We explore this perspective and show how it can be used to construct a robust approximation of the Vietoris-Rips filtration that is orders of magnitude smaller, while maintaining a log 3-interleaving unconditionally for any metric space. The resulting filtration restores near-linear scaling in the number of data points and enable us to efficiently capture homology at high degrees.

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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