Super-Polynomial Growth of the Generalized Persistence Diagram
Donghan Kim, Woojin Kim, Wonjun Lee

TL;DR
This paper demonstrates that the complexity of computing the Generalized Persistence Diagram (GPD) for multi-parameter persistence can grow faster than polynomial, indicating significant computational challenges in this area.
Contribution
The authors prove that the GPD's support can grow super-polynomially with the size of the filtration, highlighting the inherent difficulty in computing GPDs for multi-parameter persistence.
Findings
GPD support can grow faster than any polynomial in the number of simplices.
Common filtration methods can produce 'wild' filtrations with super-polynomial GPD size.
Computing GPDs is inherently complex due to super-polynomial growth.
Abstract
The Generalized Persistence Diagram (GPD) for multi-parameter persistence naturally extends the classical notion of persistence diagram for one-parameter persistence. However, unlike its classical counterpart, computing the GPD remains a significant challenge. The main hurdle is that, while the GPD is defined as the M\"obius inversion of the Generalized Rank Invariant (GRI), computing the GRI is intractable due to the formidable size of its domain, i.e., the set of all connected and convex subsets in a finite grid in with . This computational intractability suggests seeking alternative approaches to computing the GPD. In order to study the complexity associated to computing the GPD, it is useful to consider its classical one-parameter counterpart, where for a filtration of a simplicial complex with simplices, its persistence diagram contains at most …
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Taxonomy
TopicsTopological and Geometric Data Analysis
