Mathematical Programming

, Volume 116, Issue 1–2, pp 193–220

Regularization using a parameterized trust region subproblem



We present a new method for regularization of ill-conditioned problems, such as those that arise in image restoration or mathematical processing of medical data. The method extends the traditional trust-region subproblem, TRS, approach that makes use of the L-curve maximum curvature criterion, a strategy recently proposed to find a good regularization parameter. We apply a parameterized trust region approach to estimate the region of maximum curvature of the L-curve and find the regularized solution. This exploits the close connections between various parameters used to solve TRS. A MATLAB code for the algorithm is tested and a comparison to the conjugate gradient least squares, CGLS, approach is given and analysed.


Regularization Trust region subproblem Ill-conditioned problems L-curve Image restoration 


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Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  1. 1.Department of Management SciencesUniversity of WaterlooWaterlooCanada
  2. 2.Department of Combinatorics and OptimizationUniversity of WaterlooWaterlooCanada

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