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.
@InProceedings{bauer_et_al:LIPIcs.SoCG.2023.15, author = {Bauer, Ulrich and Lenzen, Fabian and Lesnick, Michael}, title = {{Efficient Two-Parameter Persistence Computation via Cohomology}}, booktitle = {39th International Symposium on Computational Geometry (SoCG 2023)}, pages = {15:1--15:17}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-273-0}, ISSN = {1868-8969}, year = {2023}, volume = {258}, editor = {Chambers, Erin W. and Gudmundsson, Joachim}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2023.15}, URN = {urn:nbn:de:0030-drops-178656}, doi = {10.4230/LIPIcs.SoCG.2023.15}, annote = {Keywords: Persistent homology, persistent cohomology, two-parameter persistence, clearing} }
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