Homology localization through the looking-glass of parameterized complexity theory

Homology localization means finding a cycle of lowest weight that represents a homology class in a simplicial complex. It is an NP-complete problem, which this paper addresses using parameterized complexity theory. We prove that to find even a constant factor approximation to this problem is W[1]-ha...

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Published in:Journal of applied and computational topology Vol. 9; no. 2
Main Authors: Blaser, Nello, Vågset, Erlend Raa
Format: Journal Article
Language:English
Published: 01.06.2025
ISSN:2367-1726, 2367-1734
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Abstract Homology localization means finding a cycle of lowest weight that represents a homology class in a simplicial complex. It is an NP-complete problem, which this paper addresses using parameterized complexity theory. We prove that to find even a constant factor approximation to this problem is W[1]-hard when solution size is used as a parameter. We have also designed and implemented two new algorithms that are fixed parameter tractable when parameterized by the treewidth of graphs associated to the simplicial complex. The running time of both algorithms matches the lower bounds we obtain from the exponential time hypothesis. We analysed the performance of the two algorithms experimentally and found that one algorithm is significantly faster than the other.
AbstractList Homology localization means finding a cycle of lowest weight that represents a homology class in a simplicial complex. It is an NP-complete problem, which this paper addresses using parameterized complexity theory. We prove that to find even a constant factor approximation to this problem is W[1]-hard when solution size is used as a parameter. We have also designed and implemented two new algorithms that are fixed parameter tractable when parameterized by the treewidth of graphs associated to the simplicial complex. The running time of both algorithms matches the lower bounds we obtain from the exponential time hypothesis. We analysed the performance of the two algorithms experimentally and found that one algorithm is significantly faster than the other.
ArticleNumber 16
Author Vågset, Erlend Raa
Blaser, Nello
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