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DOI: 10.4230/LIPIcs.SoCG.2025.68

Persistent (Co)Homology in Matrix Multiplication Time

Authors: Dmitriy Morozov and Primoz Skraba

Published in: LIPIcs, Volume 332, 41st International Symposium on Computational Geometry (SoCG 2025)

Abstract

Most algorithms for computing persistent homology do so by tracking cycles that represent homology classes. There are many choices of such cycles, and specific choices have found different uses in applications. Although it is known that persistence diagrams can be computed in matrix multiplication time for the more general case of zigzag persistent homology [Milosavljević et al., 2011], it is not clear how to extract cycle representatives, especially if specific representatives are desired. In this paper, we provide the same matrix multiplication bound for computing representatives for the two choices common in applications in the case of ordinary persistent (co)homology. We first provide a fast version of the reduction algorithm, which is simpler than the algorithm in [Milosavljević et al., 2011], but returns a different set of representatives than the standard algorithm [Edelsbrunner et al., 2002]. We then give a fast version of a variant called the row algorithm [De Silva et al., 2011], which returns the same representatives as the standard algorithm.

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Dmitriy Morozov and Primoz Skraba. Persistent (Co)Homology in Matrix Multiplication Time. In 41st International Symposium on Computational Geometry (SoCG 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 332, pp. 68:1-68:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{morozov_et_al:LIPIcs.SoCG.2025.68,
  author =	{Morozov, Dmitriy and Skraba, Primoz},
  title =	{{Persistent (Co)Homology in Matrix Multiplication Time}},
  booktitle =	{41st International Symposium on Computational Geometry (SoCG 2025)},
  pages =	{68:1--68:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-370-6},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{332},
  editor =	{Aichholzer, Oswin and Wang, Haitao},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2025.68},
  URN =		{urn:nbn:de:0030-drops-232204},
  doi =		{10.4230/LIPIcs.SoCG.2025.68},
  annote =	{Keywords: persistent homology, matrix multiplication, cycle representatives}
}

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DOI: 10.4230/LIPIcs.SoCG.2024.6

Probabilistic Analysis of Multiparameter Persistence Decompositions into Intervals

Authors: Ángel Javier Alonso, Michael Kerber, and Primoz Skraba

Published in: LIPIcs, Volume 293, 40th International Symposium on Computational Geometry (SoCG 2024)

Abstract

Multiparameter persistence modules can be uniquely decomposed into indecomposable summands. Among these indecomposables, intervals stand out for their simplicity, making them preferable for their ease of interpretation in practical applications and their computational efficiency. Empirical observations indicate that modules that decompose into only intervals are rare. To support this observation, we show that for numerous common multiparameter constructions, such as density- or degree-Rips bifiltrations, and across a general category of point samples, the probability of the homology-induced persistence module decomposing into intervals goes to zero as the sample size goes to infinity.

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Ángel Javier Alonso, Michael Kerber, and Primoz Skraba. Probabilistic Analysis of Multiparameter Persistence Decompositions into Intervals. In 40th International Symposium on Computational Geometry (SoCG 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 293, pp. 6:1-6:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{alonso_et_al:LIPIcs.SoCG.2024.6,
  author =	{Alonso, \'{A}ngel Javier and Kerber, Michael and Skraba, Primoz},
  title =	{{Probabilistic Analysis of Multiparameter Persistence Decompositions into Intervals}},
  booktitle =	{40th International Symposium on Computational Geometry (SoCG 2024)},
  pages =	{6:1--6:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-316-4},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{293},
  editor =	{Mulzer, Wolfgang and Phillips, Jeff M.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2024.6},
  URN =		{urn:nbn:de:0030-drops-199510},
  doi =		{10.4230/LIPIcs.SoCG.2024.6},
  annote =	{Keywords: Topological Data Analysis, Multi-Parameter Persistence, Decomposition of persistence modules, Poisson point processes}
}

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Documents authored by Skraba, Primoz

Persistent (Co)Homology in Matrix Multiplication Time

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Probabilistic Analysis of Multiparameter Persistence Decompositions into Intervals

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