@article{KirscheBoeckmann2006, author = {Kirsche, Andreas and B{\"o}ckmann, Christine}, title = {Pade iteration method for regularization}, series = {Applied mathematics and computation}, volume = {180}, journal = {Applied mathematics and computation}, number = {2}, publisher = {Elsevier}, address = {New York}, issn = {0096-3003}, doi = {10.1016/j.amc.2006.01.011}, pages = {648 -- 663}, year = {2006}, abstract = {In this study we present iterative regularization methods using rational approximations, in particular, Pade approximants, which work well for ill-posed problems. We prove that the (k,j)-Pade method is a convergent and order optimal iterative regularization method in using the discrepancy principle of Morozov. Furthermore, we present a hybrid Pade method, compare it with other well-known methods and found that it is faster than the Landweber method. It is worth mentioning that this study is a completion of the paper [A. Kirsche, C. Bockmann, Rational approximations for ill-conditioned equation systems, Appl. Math. Comput. 171 (2005) 385-397] where this method was treated to solve ill-conditioned equation systems. (c) 2006 Elsevier Inc. All rights reserved.}, language = {en} } @article{BoeckmannKirsche2006, author = {B{\"o}ckmann, Christine and Kirsche, Andreas}, title = {Iterative regularization method for lidar remote sensing}, issn = {0010-4655}, doi = {10.1016/j.cpc.2005.12.019}, year = {2006}, abstract = {In this paper we present an inversion algorithm for ill-posed problems arising in atmospheric remote sensing. The proposed method is an iterative Runge-Kutta type regularization method. Those methods are better well known for solving differential equations. We adapted them for solving inverse ill-posed problems. The numerical performances of the algorithm are studied by means of simulations concerning the retrieval of aerosol particle size distributions from lidar observations.}, language = {en} }