Homotopy continuation for the spectra of persistent Laplacians

Abstract

<p style='text-indent:20px;'>The <inline-formula><tex-math id="M1">\begin{document}$ p $\end{document}</tex-math></inline-formula>-persistent <inline-formula><tex-math id="M2">\begin{document}$ q $\end{document}</tex-math></inline-formula>-combinatorial Laplacian defined for a pair of simplicial complexes is a generalization of the <inline-formula><tex-math id="M3">\begin{document}$ q $\end{document}</tex-math></inline-formula>-combinatorial Laplacian. Given a filtration, the spectra of persistent combinatorial Laplacians not only recover the persistent Betti numbers of persistent homology but also provide extra multiscale geometrical information of the data. Paired with machine learning algorithms, the persistent Laplacian has many potential applications in data science. Seeking different ways to find the spectrum of an operator is an active research topic, becoming interesting when ideas are originated from multiple fields. In this work, we explore an alternative approach for the spectrum of persistent Laplacians. As the eigenvalues of a persistent Laplacian matrix are the roots of its characteristic polynomial, one may attempt to find the roots of the characteristic polynomial by homotopy continuation, and thus resolving the spectrum of the corresponding persistent Laplacian. We consider a set of simple polytopes and small molecules to prove the principle that algebraic topology, combinatorial graph, and algebraic geometry can be integrated to understand the shape of data.</p>