Publication | Open Access
On Representer Theorems and Convex Regularization
93
Citations
22
References
2019
Year
Mathematical ProgrammingConic OptimizationInverse ProblemGeneral PrincipleEngineeringVariational AnalysisRepresenter TheoremsConvex OptimizationInverse ProblemsTotal Gradient VariationFunctional AnalysisRegularization (Mathematics)Nondifferentiable OptimizationApproximation Theory
We establish a general principle which states that regularizing an inverse problem with a convex function yields solutions that are convex combinations of a small number of atoms. These atoms are identified with the extreme points and elements of the extreme rays of the regularizer level sets. An extension to a broader class of quasi-convex regularizers is also discussed. As a side result, we characterize the minimizers of the total gradient variation, which was previously an unresolved problem.
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