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Average Complexity of Matrix Reduction for Clique Filtrations

  • Ecole Polytechnique
  • Graz University of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

We study the algorithmic complexity of computing persistent homology of a randomly chosen filtration. Specifically, we prove upper bounds for the average fill-up (number of non-zero entries) of the boundary matrix on Erdös-Rényi and Vietoris-Rips filtrations after matrix reduction. Our bounds show that, in both cases, the reduced matrix is expected to be significantly sparser than what the general worst-case predicts. Our method is based on a link between the fillup of the boundary matrix and expected Betti numbers of random filtrations. Our bound for Vietoris-Rips complexes is asymptotically tight up to logarithmic factors. We also provide an Erdös-Rényi filtration realising the worst-case.

Original languageEnglish
Title of host publicationISSAC 2022 - Proceedings of the 2022 International Symposium on Symbolic and Algebraic Computation47th International Symposium on Symbolic and Algebraic Computation, ISSAC 2022
EditorsAmir Hashemi
PublisherAssociation for Computing Machinery
Pages187-196
Number of pages10
ISBN (Electronic)9781450386883
DOIs
StatePublished - Jul 4 2022
Event47th International Symposium on Symbolic and Algebraic Computation, ISSAC 2022 - Virtual, Online, France
Duration: Jul 4 2022Jul 7 2022

Publication series

NameProceedings of the International Symposium on Symbolic and Algebraic Computation, ISSAC

Conference

Conference47th International Symposium on Symbolic and Algebraic Computation, ISSAC 2022
Country/TerritoryFrance
CityVirtual, Online
Period07/4/2207/7/22

Keywords

  • average complexity
  • matrix reduction
  • persistence algorithm

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