Abstract
We present a new method for large-scale non-negative regularization based on aquadratically and non-negatively constrained quadratic problem. Such problemsarise for example in the regularization of ill posed problems in image restorationwhere the matrices involved are very ill conditioned. The method is aninterior-point iteration that requires the solution of a large-scale and possibly illconditioned parametrized trust-region subproblem at each step. The method usesrecently developed techniques for the large-scale trust-region subproblem. Wedescribe the method and present preliminary numerical results on test problemsand image restoration problems.
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