用热启动加速迭代持久同调计算
Accelerating Iterated Persistent Homology Computations with Warm Starts
摘要：
持久同源性是一种拓扑特征，用于各种应用，例如生成用于数据分析的特征和惩罚最优化问题。我们提出了一种基于更新关联矩阵分解的方法来加速在许多相似的过滤拓扑空间上进行的持久同调计算。我们的方法改进了更新方案的Cohen-Steiner，Edelsbrunner，和Morozov的排列，另外处理添加和删除的细胞在一个过滤的拓扑空间，并在一个单一的批处理的变化。我们表明，我们的计划规模的复杂性与过滤的基本变化的数量，因此往往是不太昂贵的比完整的持久性同源计算。最后，我们进行计算实验，证明实际的加速在几种情况下，包括功能生成和优化指导持久同源性。
英文摘要：
Persistent homology is a topological feature used in a variety of applications such as generating features for data analysis and penalizing optimization problems. We develop an approach to accelerate persistent homology computations performed on many similar filtered topological spaces which is based on updating associated matrix factorizations. Our approach improves the update scheme of Cohen-Steiner, Edelsbrunner, and Morozov for permutations by additionally handling addition and deletion of cells in a filtered topological space and by processing changes in a single batch. We show that the complexity of our scheme scales with the number of elementary changes to the filtration which as a result is often less expensive than the full persistent homology computation. Finally, we perform computational experiments demonstrating practical speedups in several situations including feature generation and optimization guided by persistent homology.
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