Efficient Approximation of the Matching Distance for 2-parameter persistence

12/12/2019
by   Michael Kerber, et al.
0

The matching distance is a computationally tractable topological measure to compare multi-filtered simplicial complexes. We design efficient algorithms for approximating the matching distance of two bi-filtered complexes to any desired precision ϵ>0. Our approach is based on a quad-tree refinement strategy introduced by Biasotti et al., but we recast their approach entirely in geometric terms. This point of view leads to several novel observations resulting in a practically faster algorithm. We demonstrate this speed-up by experimental comparison and provide our code in a public repository which provides the first efficient publicly available implementation of the matching distance.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/29/2018

Distributions of Matching Distances in Topological Data Analysis

In topological data analysis, we want to discern topological and geometr...
research
10/23/2022

Computing the Matching Distance of 2-Parameter Persistence Modules from Critical Values

The exact computation of the matching distance for multi-parameter persi...
research
08/12/2021

Fast Approximation of Persistence Diagrams with Guarantees

This paper presents an algorithm for the efficient approximation of the ...
research
12/21/2018

Exact computation of the matching distance on 2-parameter persistence modules

The matching distance is a pseudometric on multi-parameter persistence m...
research
02/28/2018

The ℓ^∞-Cophenetic Metric for Phylogenetic Trees as an Interleaving Distance

There are many metrics available to compare phylogenetic trees since thi...
research
02/11/2023

An EPTAS for Budgeted Matching and Budgeted Matroid Intersection

We study the budgeted versions of the well known matching and matroid in...
research
07/11/2022

A blended distance to define "people-like-me"

Curve matching is a prediction technique that relies on predictive mean ...

Please sign up or login with your details

Forgot password? Click here to reset