Journal of Convex Analysis 29 (2022), No. 4, 1251--1290
Copyright Heldermann Verlag 2022
Extremal Points of Total Generalized Variation Balls in 1D: Characterization and Applications
Josť A. Iglesias
Johann Radon Inst. for Computational and Applied Mathematics, Austrian Academy of Sciences, Linz, Austria
Johann Radon Institute for Computational and Applied Mathematics, Austrian Academy of Sciences, Linz, Austria
The total generalized variation (TGV) is a popular regularizer in inverse problems and imaging combining discontinuous solutions and higher order smoothing. In particular, empirical observations suggest that its order two version strongly favors piecewise affine functions. In the present manuscript, we formalize this statement for the one-dimensional TGV-functional by characterizing the extremal points of its sublevel sets with respect to a suitable quotient space topology. These results imply that 1D TGV-regularized linear inverse problems with finite dimensional observations admit piecewise affine minimizers. As further applications of this characterization we include precise first-order necessary optimality conditions without requiring convexity of the fidelity term, and a simple solution algorithm for TGV-regularized minimization problems.
Keywords: Total generalized variation, regularization, extremal points, non-smooth optimization, sparsity.
MSC: 46A55, 90C49, 65J20, 52A40.
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