Efficient two-parameter persistence computation via cohomology
Abstract
Clearing is a simple but effective optimization for the standard algorithm of persistent homology (PH), which dramatically improves the speed and scalability of PH computations for Vietoris--Rips filtrations. Due to the quick growth of the boundary matrices of a Vietoris--Rips filtration with increasing dimension, clearing is only effective when used in conjunction with a dual (cohomological) variant of the standard algorithm. This approach has not previously been applied successfully to the computation of two-parameter PH. We introduce a cohomological algorithm for computing minimal free resolutions of two-parameter PH that allows for clearing. To derive our algorithm, we extend the duality principles which underlie the one-parameter approach to the two-parameter setting. We provide an implementation and report experimental run times for function-Rips filtrations. Our method is faster than the current state-of-the-art by a factor of up to 20.
Cite
@article{arxiv.2303.11193,
title = {Efficient two-parameter persistence computation via cohomology},
author = {Ulrich Bauer and Fabian Lenzen and Michael Lesnick},
journal= {arXiv preprint arXiv:2303.11193},
year = {2023}
}
Comments
This is an extended version of a conference paper that appeared at SoCG 2023, see https://drops.dagstuhl.de/opus/volltexte/2023/17865