Decomposing Multiparameter Persistence Modules

Dey and Xin (J.Appl.Comput.Top., 2022) describe an algorithm to decompose finitely presented multiparameter persistence modules using a matrix reduction algorithm. Their algorithm only works for modules whose generators and relations are distinctly graded. We extend their approach to work on all finitely presented modules and introduce several improvements that lead to significant speed-ups in practice. Our algorithm is fixed-parameter tractable with respect to the maximal number of relations of the same degree and with further optimisation we obtain an O(n³) time algorithm for interval-decomposable modules. In particular, we can decide interval-decomposability in this time. As a by-product to the proofs of correctness we develop a theory of parameter restriction for persistence modules. Our algorithm is implemented as a software library aida, the first to enable the decomposition of large inputs. We show its capabilities via extensive experimental evaluation.

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Publication Details

Journal
ArXiv.org
Published
2025-01-01
DOI
https://doi.org/10.4230/lipics.socg.2025.41
Citations
1
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
1.69
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article

Decomposing Multiparameter Persistence Modules

Tamal K. Dey, Michael Kerber, Jan Jendrysiak
1 citations
ArXiv.org
Online Learning and Analytics
1.69
article

Decomposing Multiparameter Persistence Modules

Tamal K. Dey, Michael Kerber, Jan Jendrysiak
article en
1 citations

Abstract

Dey and Xin (J.Appl.Comput.Top., 2022) describe an algorithm to decompose finitely presented multiparameter persistence modules using a matrix reduction algorithm. Their algorithm only works for modules whose generators and relations are distinctly graded. We extend their approach to work on all finitely presented modules and introduce several improvements that lead to significant speed-ups in practice. Our algorithm is fixed-parameter tractable with respect to the maximal number of relations of the same degree and with further optimisation we obtain an O(n³) time algorithm for interval-decomposable modules. In particular, we can decide interval-decomposability in this time. As a by-product to the proofs of correctness we develop a theory of parameter restriction for persistence modules. Our algorithm is implemented as a software library aida, the first to enable the decomposition of large inputs. We show its capabilities via extensive experimental evaluation.

ArXiv.org
Openalex Percentile: Top 10%
Online Learning and Analytics
1.69
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Decomposing Multiparameter Persistence Modules — Tamal K. Dey, Michael Kerber, et al. · ArXiv.org (2025) | TGRS Research Map | TGRS