This paper establishes a theory of nonlinear spectral decompositions by considering
the eigenvalue problem related to an absolutely one-homogeneous functional in an
infinite-dimensional Hilbert space. This approach is 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
finite-dimensional 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
that they are 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 one-dimensional
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.