U-match factorization: sparse homological algebra, lazy cycle representatives, and dualities in persistent (co)homology

TL;DR

The paper introduces U-match, a novel sparse matrix factorization for persistent homology that significantly reduces memory usage and enables direct algebraic computations, improving efficiency in topological data analysis.

Contribution

U-match provides a new sparse matrix factorization method that allows direct algebraic operations on large, sparse matrices without decompression, enhancing TDA computations.

Findings

Reduces memory requirements by one or more orders of magnitude.

Enables direct retrieval of cycle representatives in persistent homology.

Achieves orders of magnitude faster computation of cycle representatives.

Abstract

Persistent homology is a leading tool in topological data analysis (TDA). Many problems in TDA can be solved via homological -- and indeed, linear -- algebra. However, matrices in this domain are typically large, with rows and columns numbered in billions. Low-rank approximation of such arrays typically destroys essential information; thus, new mathematical and computational paradigms are needed for very large, sparse matrices. We present the U-match matrix factorization scheme to address this challenge. U-match has two desirable features. First, it admits a compressed storage format that reduces the number of nonzero entries held in computer memory by one or more orders of magnitude over other common factorizations. Second, it permits direct solution of diverse problems in linear and homological algebra, without decompressing matrices stored in memory. These problems include look-up and retrieval of rows and columns; evaluation of birth/death times, and extraction of generators in persistent (co)homology; and, calculation of bases for boundary and cycle subspaces of filtered chain complexes. Such bases are key to unlocking a range of other topological techniques for use in TDA, and U-match factorization is designed to make such calculations broadly accessible to practitioners. As an application, we show that individual cycle representatives in persistent homology can be retrieved at time and memory costs orders of magnitude below current state of the art, via global duality. Moreover, the algebraic machinery needed to achieve this computation already exists in many modern solvers.