This image shows Andreas Langer

Andreas Langer

Priv.-Doz. Dr.

(former employee)
Institute of Applied Analysis and Numerical Simulation
Numerical Mathematics for High Performance Computing

Contact

Website

Subject

  1. 2019

    1. A. Langer and F. Gaspoz, “Overlapping domain decomposition methods for total variation denoising,” 2019. [Online]. Available: http://people.ricam.oeaw.ac.at/a.langer/publications/DDfTV_2preprint.pdf

  2. 2018

    1. M. Hintermüller, A. Langer, C. N. Rautenberg, and T. Wu, “Adaptive regularization for reconstruction from subsampled data,” in Imaging, Vision and Learning Based on Optimization and PDEs, X.-C. Tai, E. Bae, and M. Lysaker, Eds., Springer International Publishing, 2018. doi: 10.1007/978-3-319-91274-5_1.
    2. A. Langer, “Locally adaptive total variation for removing mixed Gaussian-impulse noise,” International Journal of Computer Mathematics, p. 19, 2018, [Online]. Available: https://www.tandfonline.com/doi/abs/10.1080/00207160.2018.1438603
    3. A. Langer, “Investigating the influence of box-constraints on the solution of a total variation model via an efficient primal-dual method,” Journal of Imaging, vol. 4, p. 1, 2018, [Online]. Available: http://www.mdpi.com/2313-433X/4/1/12

  3. 2017

    1. A. Langer, “Automated Parameter Selection for Total Variation Minimization in Image Restoration,” Journal of Mathematical Imaging and Vision, vol. 57, pp. 239–268, 2017, doi: 10.1007/s10851-016-0676-2.
    2. A. Langer, “Automated Parameter Selection in the $L^1$-$L^2$-TV Model for Removing Gaussian Plus Impulse Noise,” Inverse Problems, vol. 33, p. 41, 2017, [Online]. Available: http://people.ricam.oeaw.ac.at/a.langer/publications/L1L2TVm.pdf
    3. M. Alkämper and A. Langer, “Using DUNE-ACFem for Non-smooth Minimization of Bounded Variation Functions,” Archive of Numerical Software, vol. 5, Art. no. 1, 2017, [Online]. Available: https://journals.ub.uni-heidelberg.de/index.php/ans/article/view/27475
    4. M. Hintermüller, C. N. Rautenberg, T. Wu, and Andreas Langer, “Optimal Selection of the Regularization Function in a Weighted Total Variation Model. Part II: Algorithm, Its Analysis and Numerical Tests,” Journal of Mathematical Imaging and Vision, pp. 1–19, 2017, [Online]. Available: https://link.springer.com/article/10.1007/s10851-017-0736-2

  4. 2015

    1. M. Hintermüller and A. Langer, “Non-overlapping domain decomposition methods for dual total variation based image denoising,” Journal of Scientific Computing, vol. 62, Art. no. 2, 2015, [Online]. Available: http://link.springer.com/article/10.1007/s10915-014-9863-8

  5. 2014

    1. M. Hintermüller and A. Langer, Adaptive Regularization for Parseval Frames in Image Processing. SFB-Report No. 2014-014, 2014. [Online]. Available: http://people.ricam.oeaw.ac.at/a.langer/publications/SFB-Report-2014-014.pdf
    2. M. Hintermüller and A. Langer, “Surrogate Functional Based Subspace Correction Methods for Image Processing,” in Domain Decomposition Methods in Science and Engineering XXI, Springer, 2014, pp. 829–837. [Online]. Available: http://link.springer.com/chapter/10.1007/978-3-319-05789-7_80

  6. 2013

    1. M. Hintermüller and A. Langer, “Subspace Correction Methods for a Class of Nonsmooth and Nonadditive Convex Variational Problems with Mixed $L^1$/$L^2$ Data-Fidelity in Image Processing,” SIAM Journal on Imaging Sciences, vol. 6, Art. no. 4, 2013, [Online]. Available: http://epubs.siam.org/doi/abs/10.1137/120894130
    2. A. Langer, S. Osher, and C.-B. Schönlieb, “Bregmanized domain decomposition for image restoration,” Journal of Scientific Computing, vol. 54, Art. no. 2, 2013, [Online]. Available: http://link.springer.com/article/10.1007/s10915-012-9603-x

  7. 2012

    1. M. Fornasier, Y. Kim, A. Langer, and C.-B. Schönlieb, “Wavelet Decomposition Method for $L_2$/TV-Image Deblurring,” SIAM Journal on Imaging Sciences, vol. 5, Art. no. 3, 2012, [Online]. Available: http://epubs.siam.org/doi/abs/10.1137/100819801

  8. 2010

    1. M. Fornasier, A. Langer, and C.-B. Schönlieb, “A convergent overlapping domain decomposition method for total variation minimization,” Numerische Mathematik, vol. 116, Art. no. 4, 2010, [Online]. Available: http://link.springer.com/article/10.1007/s00211-010-0314-7

  9. 2009

    1. M. Fornasier, A. Langer, and C.-B. Schönlieb, “Domain decomposition methods for compressed sensing,” in Proceedings of the International Conference of SampTA09, 2009. [Online]. Available: http://arxiv.org/abs/0902.0124

Summer term 2020

  • Mathematische Programmierung für Lehramt
  • Numerical Methods for differential equations

Winter term 2019/20

  • Computerpraktikum für den Bachelor

Summer term 2019

  • Mathematische Programmierung 2
  • Numerical methods for differential equations

Winter term 2018/19

  • Programmierkurs für den Bachelor

Summer term 2018

  • Numerical Methods for Differential Equations
  • Fortgeschrittene Analysis für SimTech 2

Winter term 2017/18

  • Seminar: Multifield Problems in Image Analysis and Visualization
  • Einführung in die Optimierung

Summer term 2017

  • Grundlagen inverser Probleme
  • Weiterführende Numerik partieller Differentialgleichungen

Winter term 2016/17

  • Einführung in die Optimierung
  • Seminar: Mathematische Probleme in der Bildbearbeitung

Summer term 2016

  • Höhere Mathematik 2 (Assistant)

Winter term 2015/16

  • Höhere Mathematik 3 (Assistant)

Summer term 2015

  • Numerische Mathematik 2 (Assistant)

Winter term 2014/15

  • Numerische Mathematik 1 (Assistant)

Summer term 2014

  • Numerische Mathematik 2 (Assistant)
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Numerical Mathematics for High Performance Computing

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