English

On the Dual Geometry of Laplacian Eigenfunctions

Signal Processing 2018-04-27 v1 Machine Learning Analysis of PDEs Spectral Theory

Abstract

We discuss the geometry of Laplacian eigenfunctions Δϕ=λϕ-\Delta \phi = \lambda \phi on compact manifolds (M,g)(M,g) and combinatorial graphs G=(V,E)G=(V,E). The 'dual' geometry of Laplacian eigenfunctions is well understood on Td\mathbb{T}^d (identified with Zd\mathbb{Z}^d) and Rn\mathbb{R}^n (which is self-dual). The dual geometry is of tremendous role in various fields of pure and applied mathematics. The purpose of our paper is to point out a notion of similarity between eigenfunctions that allows to reconstruct that geometry. Our measure of 'similarity' α(ϕλ,ϕμ) \alpha(\phi_{\lambda}, \phi_{\mu}) between eigenfunctions ϕλ\phi_{\lambda} and ϕμ\phi_{\mu} is given by a global average of local correlations α(ϕλ,ϕμ)2=ϕλϕμL22M(Mp(t,x,y)(ϕλ(y)ϕλ(x))(ϕμ(y)ϕμ(x))dy)2dx, \alpha(\phi_{\lambda}, \phi_{\mu})^2 = \| \phi_{\lambda} \phi_{\mu} \|_{L^2}^{-2}\int_{M}{ \left( \int_{M}{ p(t,x,y)( \phi_{\lambda}(y) - \phi_{\lambda}(x))( \phi_{\mu}(y) - \phi_{\mu}(x)) dy} \right)^2 dx}, where p(t,x,y)p(t,x,y) is the classical heat kernel and etλ+etμ=1e^{-t \lambda} + e^{-t \mu} = 1. This notion recovers all classical notions of duality but is equally applicable to other (rough) geometries and graphs; many numerical examples in different continuous and discrete settings illustrate the result.

Cite

@article{arxiv.1804.09816,
  title  = {On the Dual Geometry of Laplacian Eigenfunctions},
  author = {Alexander Cloninger and Stefan Steinerberger},
  journal= {arXiv preprint arXiv:1804.09816},
  year   = {2018}
}
R2 v1 2026-06-23T01:36:09.854Z