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2014 | OriginalPaper | Buchkapitel

Clear and Compress: Computing Persistent Homology in Chunks

verfasst von : Ulrich Bauer, Michael Kerber, Jan Reininghaus

Erschienen in: Topological Methods in Data Analysis and Visualization III

Verlag: Springer International Publishing

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Abstract

We present a parallel 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 further improve the performance through parallel computation.

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Fußnoten
1
The running time of the third step could be lowered to g ω , where ω is the matrix-multiplication exponent, using the method of [17].
 
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Metadaten
Titel
Clear and Compress: Computing Persistent Homology in Chunks
verfasst von
Ulrich Bauer
Michael Kerber
Jan Reininghaus
Copyright-Jahr
2014
DOI
https://doi.org/10.1007/978-3-319-04099-8_7

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