The Generalized Rank Invariant: M\"obius invertibility, Discriminating Power, and Connection to Other Invariants
Nathaniel Clause, Woojin Kim, Facundo M\'emoli

TL;DR
This paper introduces the generalized rank invariant (GRI) for multi-parameter persistent homology, explores its M"obius invertibility, and connects it to other invariants, aiming to improve quantification and computational efficiency.
Contribution
It extends the rank invariant to the GRI, axiomatizes its M"obius invertibility, and links it to motivic invariants, enhancing understanding and computation in multi-parameter persistence.
Findings
M"obius invertibility characterizes the GRI's information content.
The GRI can be encoded as a persistence diagram under certain restrictions.
Many existing invariants can be derived from or recast as motivic invariants.
Abstract
In addition to inherent computational challenges, the absence of a canonical method for quantifying `persistence' in multi-parameter persistent homology remains a hurdle in its application. One of the best known quantifications of persistence for multi-parameter persistent homology is the rank invariant, which has recently evolved into the generalized rank invariant (GRI) by naturally extending its domain. This extension enables us to quantify persistence across a broader range of regions in the indexing poset compared to the rank invariant. However, the size of the domain of the GRI is generally formidable, making it desirable to restrict its domain to a more manageable subset for computational purposes. The foremost questions regarding such a restriction of the domain are: (1) How to restrict, if possible, the domain of the GRI without any loss of information? (2) When can we more…
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Taxonomy
TopicsMulti-Criteria Decision Making
