@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{BoeckmannOsterloh2014,
  author    = {B{\"o}ckmann, Christine and Osterloh, Lukas},
  title     = {Runge-Kutta type regularization method for inversion of spheroidal particle distribution from limited optical data},
  series = {Inverse problems in science and engineering},
  volume    = {22},
  journal   = {Inverse problems in science and engineering},
  number    = {1},
  publisher = {Routledge, Taylor \& Francis Group},
  address   = {Abingdon},
  issn      = {1741-5977},
  doi       = {10.1080/17415977.2013.830615},
  pages     = {150 -- 165},
  year      = {2014},
  abstract  = {The Runge-Kutta type regularization method was recently proposed as a potent tool for the iterative solution of nonlinear ill-posed problems. In this paper we analyze the applicability of this regularization method for solving inverse problems arising in atmospheric remote sensing, particularly for the retrieval of spheroidal particle distribution. Our numerical simulations reveal that the Runge-Kutta type regularization method is able to retrieve two-dimensional particle distributions using optical backscatter and extinction coefficient profiles, as well as depolarization information.},
  language  = {en}
}
@misc{BoeckmannOsterloh2014,
  author    = {B{\"o}ckmann, Christine and Osterloh, Lukas},
  title     = {Runge-Kutta type regularization method for inversion of spheroidal particle distribution from limited optical data},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {907},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-44120},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-441200},
  pages     = {150 -- 165},
  year      = {2014},
  abstract  = {The Runge-Kutta type regularization method was recently proposed as a potent tool for the iterative solution of nonlinear ill-posed problems. In this paper we analyze the applicability of this regularization method for solving inverse problems arising in atmospheric remote sensing, particularly for the retrieval of spheroidal particle distribution. Our numerical simulations reveal that the Runge-Kutta type regularization method is able to retrieve two-dimensional particle distributions using optical backscatter and extinction coefficient profiles, as well as depolarization information.},
  language  = {en}
}