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Square regularization matrices for large linear discrete ill-posed problems

Authors


Lothar Reichel, Department of Mathematical Sciences, Kent State University, Kent, OH 44242, USA.

E-mail: reichel@math.kent.edu

SUMMARY

Large linear discrete ill-posed problems with contaminated data are often solved with the aid of Tikhonov regularization. Commonly used regularization matrices are finite difference approximations of a suitable derivative and are rectangular. This paper discusses the design of square regularization matrices that can be used in iterative methods based on the Arnoldi process for large-scale Tikhonov regularization problems. Copyright © 2012 John Wiley & Sons, Ltd.

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