AN EFFICIENT ITERATIVE APPROACH FOR LARGE-SCALE SEPARABLE NONLINEAR INVERSE PROBLEMS

Julianne Chung

doi:10.1137/080732213

2010-01-01

PRE

Julianne Chung *

James G. Nagy

DOI：10.1137/080732213

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摘要

En 中文

We present an efficient iterative approach to solving separable nonlinear least squares problems that arise in large-scale inverse problems. A variable projection Gauss-Newton method is used to solve the nonlinear least squares problem, and Tikhonov regularization is incorporated using an iterative hybrid scheme. Regularization parameters are chosen automatically using a weighted generalized cross validation method, thus providing a nonlinear solver that requires very little input from the user. Applications from image deblurring and digital tomosynthesis illustrate the effectiveness of the resulting numerical scheme.

Keyword:

Gauss-Newton method

ill-posed inverse problems

iterative methods

Golub-Kahan bidiagonalization

hybrid method

Tikhonov regularization

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期刊

SIAM Journal on Scientific Computing

IF:

2.6

论文数:

5.1K

被引数:

1.8W

机构

University System of Maryland

学者数:

6.5W

论文数: 5.6W

被引数: 113

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