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Elastic-net regularization: error estimates and active set methods. (English) Zbl 1188.49026

Summary: This paper investigates theoretical properties and efficient numerical algorithms for the so-called elastic-net regularization originating from statistics, which enforces simultaneously \(\ell^1\) and \(\ell^2\) regularization. The stability of the minimizer and its consistency are studied, and convergence rates for both a priori and a posteriori parameter choice rules are established. Two iterative numerical algorithms of active set type are proposed, and their convergence properties are discussed. Numerical results are presented to illustrate the features of the functional and the algorithms.

MSC:

49K40 Sensitivity, stability, well-posedness
49M30 Other numerical methods in calculus of variations (MSC2010)
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