Clear and Compress: Computing Persistent Homology in Chunks


We present a parallelizable algorithm for computing the persistent homology of a filtered chain complex. Our approach differs from the commonly used reduction algorithm by first computing persistence pairs within local chunks, then simplifying the unpaired columns, and finally applying standard reduction on the simplified matrix. The approach generalizes a technique by Günther et al., which uses discrete Morse Theory to compute persistence; we derive the same worst-case complexity bound in a more general context. The algorithm employs several practical optimization techniques which are of independent interest. Our sequential implementation of the algorithm is competitive with state-of-the-art methods, and we improve the performance through parallelized computation.

DOI: 10.1007/978-3-319-04099-8_7

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@inproceedings{Bauer2014ClearAC, title={Clear and Compress: Computing Persistent Homology in Chunks}, author={Ulrich Bauer and Michael Kerber and Jan Reininghaus}, booktitle={Topological Methods in Data Analysis and Visualization}, year={2014} }