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The constrained least-squares regularization of nonlinear ill-posed problems is a nonlinear programming problem for which trust-region methods have been developed. In this paper we complement the convergence theory of one of those methods showing that, under suitable hypotheses, local (superlinear or quadratic) convergence holds and every accumulation point is second-order stationary.

New convergence results on an algorithm for norm constrained regularization and related problems