An Analysis of the Zero-Crossing Method for Choosing Regularization Parameters
نویسندگان
چکیده
منابع مشابه
An Analysis of the Zero-Crossing Method for Choosing Regularization Parameters
Solving discrete ill-posed problems via Tikhonov regularization introduces the problem of determining a regularization parameter. There are several methods available for choosing such a parameter, yet, in general, the uniqueness of this choice is an open question. Two empirical methods for determining a regularization parameter (which appear in the biomedical engineering literature) are the com...
متن کاملA Mathematical Analysis of New L-curve to Estimate the Parameters of Regularization in TSVD Method
A new technique to find the optimization parameter in TSVD regularization method is based on a curve which is drawn against the residual norm [5]. Since the TSVD regularization is a method with discrete regularization parameter, then the above-mentioned curve is also discrete. In this paper we present a mathematical analysis of this curve, showing that the curve has L-shaped path very similar t...
متن کاملResidual periodograms for choosing regularization parameters for ill-posed problems
Bert W. Rust and Dianne P. O’Leary Mathematical and Computational Sciences Division, National Institute of Standards and Technology, Gaithersburg, MD 20899. [email protected] Computer Science Department and Institute for Advanced Computer Studies, University of Maryland, College Park, MD 20742; [email protected]. Mathematical and Computational Sciences Division, National Institute of Standards...
متن کاملMonte-Carlo SURE for Choosing Regularization Parameters in Image Deblurring
Parameter choice is crucial to regularization-based image deblurring. In this paper, a Monte Carlo method is used to approximate the optimal regularization parameter in the sense of Stein’s unbiased risk estimate (SURE) which has been applied to image deblurring. The proposed algorithm is suitable for the exact deblurring functions as well as those of not being expressed analytically. We justif...
متن کاملChoosing Regularization Parameters in Iterative Methods for Ill-Posed Problems
Numerical solution of ill-posed problems is often accomplished by discretization (projection onto a finite dimensional subspace) followed by regularization. If the discrete problem has high dimension, though, typically we compute an approximate solution by projecting the discrete problem onto an even smaller dimensional space, via iterative methods based on Krylov subspaces. In this work we pre...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SIAM Journal on Scientific Computing
سال: 2002
ISSN: 1064-8275,1095-7197
DOI: 10.1137/s1064827500373516