All Stories

  1. Semi-heuristic parameter choice rules for Tikhonov regularisation with operator perturbations

    Article • Journal of Inverse and Ill-Posed Problems, February 2019, De Gruyter

    Uno Hämarik

  2. Erratum: Regularization by discretization in Banach spaces (2016Inverse Problems32035004)

    Article • Inverse Problems, April 2016, Institute of Physics Publishing

    Uno Hämarik

  3. Regularization by discretization in Banach spaces

    Article • Inverse Problems, February 2016, Institute of Physics Publishing

    Uno Hämarik

  4. On comparison of accuracy of approximate solutions of operator equations with noisy data

    Article • January 2016, American Institute of Physics

    Uno Hämarik

  5. On the Self-Regularization of Ill-Posed Problems by the Least Error Projection Method

    Article • Mathematical Modelling and Analysis, May 2014, Vilnius Gediminas Technical University

    Uno Hämarik

  6. Monotonicity of error of regularized solution and its use for parameter choice

    Article • Inverse Problems in Science and Engineering, August 2013, Taylor & Francis

    Uno Hämarik

  7. A family of rules for parameter choice in Tikhonov regularization of ill-posed problems with inexact noise level

    Article • Journal of Computational and Applied Mathematics, February 2012, Elsevier

    Uno Hämarik

  8. A family of rules for the choice of the regularization parameter in the Lavrentiev method in the case of rough estimate of the noise level of the data

    Article • Journal of Inverse and Ill-Posed Problems, January 2012, De Gruyter

    Uno Hämarik

  9. On the quasi-optimal rules for the choice of the regularization parameter in case of a noisy operator

    Article • Advances in Computational Mathematics, September 2011, Springer Science + Business Media

    Uno Hämarik

  10. Comparison of parameter choices in regularization algorithms in case of different information about noise level

    Article • CALCOLO, September 2010, Springer Science + Business Media

    Uno Hämarik

  11. Extrapolation of Tikhonov regularization method

    Article • Mathematical Modelling and Analysis, January 2010, Vilnius Gediminas Technical University

    Uno Hämarik

  12. Foreword

    Article • Numerical Functional Analysis and Optimization, December 2009, Taylor & Francis

    Uno Hämarik

  13. A Tribute to Gennadi Vainikko

    Article • Numerical Functional Analysis and Optimization, December 2009, Taylor & Francis

    Uno Hämarik

  14. About the Balancing Principle for Choice of the Regularization Parameter

    Article • Numerical Functional Analysis and Optimization, December 2009, Taylor & Francis

    Uno Hämarik

  15. On numerical realization of quasioptimal parameter choices in (iterated) Tikhonov and Lavrentiev regularization

    Article • Mathematical Modelling and Analysis, January 2009, Vilnius Gediminas Technical University

    Uno Hämarik

  16. New rule for choice of the regularization parameter in (iterated) tikhonov method

    Article • Mathematical Modelling and Analysis, January 2009, Vilnius Gediminas Technical University

    Uno Hämarik

  17. Extrapolation of Tikhonov and Lavrentiev regularization methods

    Article • Journal of Physics Conference Series, November 2008, Institute of Physics Publishing

    Uno Hämarik

  18. About the balancing principle for choice of the regularization parameter

    Article • Journal of Physics Conference Series, November 2008, Institute of Physics Publishing

    Uno Hämarik

  19. Gennadi Vainikko — 70

    Article • Computational Methods in Applied Mathematics, January 2008, De Gruyter

    Uno Hämarik

  20. On The Analog Of The Monotone Error Rule For Parameter Choice In The (Iterated) Lavrentiev Regularization

    Article • Computational Methods in Applied Mathematics, January 2008, De Gruyter

    Uno Hämarik

  21. Use of extrapolation in regularization methods

    Article • Journal of Inverse and Ill-Posed Problems, June 2007, De Gruyter

    Uno Hämarik

  22. On rules for stopping the conjugate gradient type methods in ill‐posed problems

    Article • Mathematical Modelling and Analysis, January 2007, Vilnius Gediminas Technical University

    Uno Hämarik

  23. On the Choice of the Regularization Parameter in the Case of the Approximately Given Noise Level of Data

    Article • January 2004, Springer Science + Business Media

    Uno Hämarik

  24. The use of monotonicity for choosing the regularization parameter in ill-posed problems

    Article • Inverse Problems, December 1999, Institute of Physics Publishing

    Uno Hämarik

  25. On the monotone error rule for choosing the regularization parameter in ill–posed problems

    Article • De Gruyter

    Uno Hämarik