Regularization using a parameterized trust region subproblem
成果类型：
Article; Proceedings Paper
署名作者：
Grodzevich, Oleg; Wolkowicz, Henry
署名单位：
University of Waterloo; University of Waterloo
刊物名称：
MATHEMATICAL PROGRAMMING
ISSN/ISSBN：
0025-5610
DOI：
10.1007/s10107-007-0126-4
发表日期：
2009
页码：
193-220
关键词：
摘要：
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.
来源URL：