Nonlinear Spectral Decompositions by Gradient Flows of OneHomogeneous Functionals
Abstract
This paper establishes a theory of nonlinear spectral decompositions by considering the eigenvalue problem related to an absolutely onehomogeneous functional in an infinitedimensional Hilbert space. This approach is both motivated by works for the total variation, where interesting results on the eigenvalue problem and the relation to the total variation flow have been proven previously, and by recent results on finitedimensional polyhedral seminorms, where gradient flows can yield spectral decompositions into eigenvectors. We provide a geometric characterization of eigenvectors via a dual unit ball and prove them to be subgradients of minimal norm. This establishes the connection to gradient flows, whose time evolution is a decomposition of the initial condition into subgradients of minimal norm. If these are eigenvectors, this implies an interesting orthogonality relation and the equivalence of the gradient flow to a variational regularization method and an inverse scale space flow. Indeed we verify that all scenarios where these equivalences were known before by other arguments  such as onedimensional total variation, multidimensional generalizations to vector fields, or certain polyhedral seminorms  yield spectral decompositions, and we provide further examples. We also investigate extinction times and extinction profiles, which we characterize as eigenvectors in a very general setting, generalizing several results from literature.
 Publication:

arXiv eprints
 Pub Date:
 January 2019
 arXiv:
 arXiv:1901.06979
 Bibcode:
 2019arXiv190106979B
 Keywords:

 Mathematics  Analysis of PDEs;
 Mathematics  Spectral Theory;
 35P10;
 35P30;
 47J10
 EPrint:
 40 pages, 2 figures, version as published in Analysis &