Abstract
Many estimation problems amount to minimizing a piecewise Cm objective function, with m ≥ 2, composed of a quadratic data-fidelity term and a general regularization term. It is widely accepted that the minimizers obtained using non-convex and possibly non-smooth regularization terms are frequently good estimates. However, few facts are known on the ways to control properties of these minimizers. This work is dedicated to the stability of the minimizers of such objective functions with respect to variations of the data. It consists of two parts: first we consider all local minimizers, whereas in a second part we derive results on global minimizers. In this part we focus on data points such that every local minimizer is isolated and results from a Cm-1 local minimizer function, defined on some neighborhood. We demonstrate that all data points for which this fails form a set whose closure is negligible.
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Durand, S., Nikolova, M. Stability of the Minimizers of Least Squares with a Non-Convex Regularization. Part I: Local Behavior. Appl Math Optim 53, 185–208 (2006). https://doi.org/10.1007/s00245-005-0842-1
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DOI: https://doi.org/10.1007/s00245-005-0842-1