L- and Θ-curve approaches for the selection of regularization parameter in geophysical diffraction tomography

2007 ◽  
Vol 33 (5) ◽  
pp. 618-629 ◽  
Author(s):  
E.T.F. Santos ◽  
A. Bassrei
Keyword(s):  
Regularization Parameter ◽  
Diffraction Tomography ◽  
Selection Of

Selection of regularization parameter for optical topography

2009 ◽  
Vol 14 (3) ◽  
pp. 034044 ◽  
Author(s):  
Teresa Correia ◽  
Adam Gibson ◽  
Martin Schweiger ◽  
Jeremy Hebden
Keyword(s):  
Regularization Parameter ◽  
Optical Topography ◽  
Selection Of

scholarly journals COMPLEXITY ESTIMATES FOR SEVERELY ILL-POSED PROBLEMS UNDER A POSTERIORI SELECTION OF REGULARIZATION PARAMETER

2017 ◽  
Vol 22 (3) ◽  
pp. 283-299
Author(s):  
Sergii G. Solodky ◽  
Ganna L. Myleiko ◽  
Evgeniya V. Semenova
Keyword(s):  
Integral Equations ◽  
Regularization Parameter ◽  
Efficient Algorithms ◽  
Optimal Order ◽  
A Posteriori ◽  
Order Of Accuracy ◽  
Tikhonov Method ◽  
Discrete Information ◽  
Ill Posed ◽  
Selection Of

In the article the authors developed two efficient algorithms for solving severely ill-posed problems such as Fredholm’s integral equations. The standard Tikhonov method is applied as a regularization. To select a regularization parameter we employ two different a posteriori rules, namely, discrepancy and balancing principles. It is established that proposed strategies not only achieved optimal order of accuracy on the class of problems under consideration, but also they are economical in the sense of used discrete information.

Selection of regularization parameter for l1-regularized damage detection

2018 ◽  
Vol 423 ◽  
pp. 141-160 ◽  
Author(s):  
Rongrong Hou ◽  
Yong Xia ◽  
Yuequan Bao ◽  
Xiaoqing Zhou
Keyword(s):  
Damage Detection ◽  
Regularization Parameter ◽  
Selection Of
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