Publications of AG Inverse Problems and Imaging

Monographies (2)

1. K. Bredies, D. Lorenz.
   Mathematical Image Processing.
   Applied and Numerical Harmonic Analysis, Birkhäuser, 2018.

   DOI: 10.1007/978-3-030-01458-2

2. K. Bredies, D. Lorenz.
   Mathematische Bildverarbeitung:.
   , Vieweg Verlag, 2011.

Articles (76)

1. R. Gower, D. Lorenz, M. Winkler.
   A Bregman-Kaczmarz method for nonlinear systems of equations.
   Computational Optimization and Applications, 87:1059-1098, 2024.

   DOI: 10.1007/s10589-023-00541-9
   online at: https://arxiv.org/abs/2303.08549

2. L. Tondji, I. Necoara, D. Lorenz.
   Acceleration and restart for the randomized Bregman-Kaczmarz method.
   Linear Algebra and its Applications, 699:508-538, 2024.

   DOI: 10.1016/j.laa.2024.07.009
   online at: https://arxiv.org/abs/2310.17338

3. L. Tondji, I. Tondji, D. Lorenz.
   Adaptive Bregman-Kaczmarz: An approach to solve linear inverse problems with independent noise exactly.
   Inverse Problems, 40(9), 095006, IOPscience, 2024.

   DOI: 10.1088/1361-6420/ad5fb1
   online at: https://arxiv.org/abs/2309.06186

4. M. Upadhyaya, S. Banert, A. B. Taylor, P. Giselsson.
   Automated tight Lyapunov analysis for first-order methods.
   Erscheint in Mathematical Programming

   online at: https://arxiv.org/abs/2302.06713

5. C. Brauer, N. Breustedt, T. de Wolff, D. Lorenz.
   Learning variational models with unrolling and bilevel optimization.
   Analysis and Applications, 22(3):569-617, World Scientific, 2024.

   DOI: 10.1142/S0219530524400037
   online at: https://arxiv.org/abs/2209.12651

6. D. Lorenz, J. Marquardt, E. Naldi.
   The degenerate variable metric proximal point algorithm and adaptive stepsizes for primal-dual Douglas-Rachford.
   Optimization, :1-27, Taylor & Francis, 2024.

   DOI: 10.1080/02331934.2024.2325552
   online at: https://arxiv.org/abs/2302.13128

7. S. Banert, O. Öktem, J. Adler, J. Rudzusika.
   Accelerated Forward-Backward Optimization using Deep Learning.
   Erscheint in SIAM Journal on Optimization

   online at: https://arxiv.org/abs/2105.05210

8. D. Lorenz, F. Schneppe.
   Chambolle-Pock’s primal-dual method with mismatched adjoint.
   Applied Mathematics & Optimization, 87(22), 2023.

   DOI: 10.1007/s00245-022-09933-5
   online at: https://arxiv.org/abs/2201.04928

9. N. K. Bellam Muralidhar, C. Gräßle, N. Rauter, A. Mikhaylenko, R. Lammering, D. Lorenz.
   Damage identification in fiber metal laminates using bayesian analysis with model order reduction.
   Computer Methods in Applied Mechanics and Engineering, Part B 403, 2023.

   DOI: 10.1016/j.cma.2022.115737
   online at: https://arxiv.org/abs/2206.04329

10. D. Nganyu Tanyu, J. Ning, T. Freudenberg, N. Heilenkötter, A. Rademacher, U. Iben, P. Maaß.
    Deep learning methods for partial differential equations and related parameter identification problems.
    Inverse Problems, 39(10), 2023.

    DOI: 10.1088/1361-6420/ace9d4

11. S. Banert, P. Giselsson, H. Sadeghi.
    Incorporating history and deviations in forward–backward splitting.
    Numerical Algorithms, , 2023.

    DOI: 10.1007/s11075-023-01686-8

12. D. Lorenz, F. Schneppe, L. Tondji.
    Linearly convergent adjoint free solution of least squares problems by random descent.
    Inverse Problems, 39(12), 125019, 2023.

    DOI: 10.1088/1361-6420/ad08ed
    online at: https://arxiv.org/abs/2306.01946

13. S. Banert, P. Giselsson, M. Morin.
    Nonlinear Forward-Backward Splitting with Momentum Correction.
    Set-Valued and Variational Analysis, 31(37), 2023.

    DOI: 10.1007/s11228-023-00700-4

14. A. Ebner, J. Frikel, D. Lorenz, J. Schwab, M. Haltmeier.
    Regularization of inverse problems by filtered diagonal frame decomposition.
    Applied and Computational Harmonic Analysis, 62:66-83, 2023.

    DOI: 10.1016/j.acha.2022.08.005
    online at: https://arxiv.org/abs/2008.06219

15. K. Bredies, E. Chenchene, D. Lorenz, E. Naldi.
    Degenerate preconditioned proximal point algorithms.
    SIAM Journal on Optimization, 32(3), 2022.

    DOI: 10.1137/21M1448112
    online at: https://arxiv.org/abs/2109.11481

16. F. Schöpfer, D. Lorenz, L. Tondji, M. Winkler.
    Extended randomized Kaczmarz method for sparse least squares and impulsive noise problems.
    Linear Algebra and its Applications, 652:132-154, 2022.

    DOI: 10.1016/j.laa.2022.07.003
    online at: https://arxiv.org/abs/2201.08620

17. D. Lorenz, L. Tondji.
    Faster randomized block sparse Kaczmarz by averaging.
    Numerical Algorithms, 91:1417-1451, 2022.

    DOI: 10.1007/s11075-022-01473-x
    online at: https://arxiv.org/abs/2203.10838

18. D. Nganyu Tanyu, D. Schulz, T. Tatietse, T. Lukong.
    Long Term Electricity Load Forecast Based on Machine Learning for Cameroon’s Power System.
    Energy and Environment Research, 12(1), 2022.

    DOI: 10.5539/eer.v12n1p45
    online at: https://ccsenet.org/journal/index.php/eer/article/view/0/47276

19. D. Ghilli, D. Lorenz, E. Resmerita.
    Nonconvex flexible sparsity regularization: theory and monotone numerical schemes.
    Optimization, 71(4):1114-1140, 2022.

    DOI: 10.1080/02331934.2021.2011869
    online at: https://arxiv.org/abs/2111.06281

20. A. Mikhaylenko, N. Rauter, N. K. Bellam Muralidhar, T. Barth, D. Lorenz, R. Lammering.
    Numerical analysis of the main wave propagation characteristics in a steel-CFRP laminate including model order reduction.
    Acoustics, 4(3):517-537, 2022.

    DOI: 10.3390/acoustics4030032
    online at: https://arxiv.org/abs/2206.04329

21. D. Lorenz, H. Mahler.
    Orlicz space regularization of continuous optimal transport problems.
    Applied Mathematics & Optimization, 85(14), 2022.

    DOI: 10.1007/s00245-022-09826-7
    online at: https://arxiv.org/abs/2004.11574

22. C. Clason, D. Lorenz, H. Mahler, B. Wirth.
    Entropic regularization of continuous optimal transport problems.
    Journal of Mathematical Analysis and Applications, 494(1), 2021.

    DOI: 10.1016/j.jmaa.2020.124432
    online at: http://arxiv.org/abs/1803.02848

23. N. K. Bellam Muralidhar, N. Rauter, A. Mikhaylenko, R. Lammering, D. Lorenz.
    Parametric model order reduction of guided ultrasonic wave propagation in fiber metal laminates with damage.
    Modelling, 2(4):591-608, 2021.

    DOI: 10.3390/modelling2040031
    online at: https://www.preprints.org/manuscript/202109.0312/v1

24. D. Lorenz, C. Meyer, P. Manns.
    Quadratically regularized optimal transport.
    Applied Mathematics & Optimization, 83:1919-1949, 2021.

    DOI: 10.1007/s00245-019-09614-w
    online at: https://arxiv.org/abs/1903.01112

25. C. Brauer, D. Lorenz.
    Complexity and applications of the homotopy principle for uniformly constrained sparse minimization.
    Applied Mathematics & Optimization, 82(3):1-34, 2020.

    DOI: 10.1007/s00245-019-09565-2

26. B. Komander, D. Lorenz, L. Vestweber.
    Denoising of image gradients and to- tal generalized variation denoising.
    Journal of Mathematical Imaging and Vision, 61(1):21-39, 2019.

    DOI: 10.1007/s10851-018-0819-8
    online at: http://arxiv.org/abs/1712.08585

27. F. Schöpfer, D. Lorenz.
    Linear convergence of the Randomized Sparse Kaczmarz method.
    Mathematical Programming, 173(1):509-536, 2019.

    DOI: 10.1007/s10107-017-1229-1
    online at: http://arxiv.org/abs/1610.02889

28. D. Lorenz, Q. Tran-Dinh.
    Non-stationary Douglas-Rachford and alternating direction method of multipliers: adaptive stepsizes and convergence.
    Computational Optimization and Applications, 74(1):67-92, 2019.

    DOI: 10.1007/s10589-019-00106-9
    online at: http://arxiv.org/abs/1801.03765

29. C. Brauer, D. Lorenz, A. Tillmann.
    A primal-dual homotopy algorithm for ℓ1 -minimization with ℓ∞ -constraints.
    Computational Optimization and Applications, 70:443-478, 2018.

    DOI: 10.1007/s10589-018-9983-4
    online at: http://arxiv.org/abs/1601.10022

30. D. Lorenz, L. M. Mescheder.
    An extended Perona-Malik model based on probabilistic models.
    Journal of Mathematical Imaging and Vision, 60(1):128-144, 2018.

    DOI: 10.1007/s10851-017-0746-0
    online at: http://arxiv.org/abs/1612.06176

31. D. Lorenz, K. Wirths.
    Sarrus rules for matrix determinants and dihedral groups.
    The College Mathematics Journal, 49(5):333-340, 2018.

    DOI: 10.1080/07468342.2018.1526019
    online at: https://arxiv.org/abs/1809.08948

32. D. Lorenz, S. Rose, F. Schöpfer.
    The randomized Kaczmarz method with mismatched adjoint.
    BIT Numerical Mathematics, 48(4):1079-1098, 2018.

    DOI: 10.1007/s10543-018-0717-x
    online at: http://arxiv.org/abs/1803.02848

33. D. Lorenz, J. Lorenz, C. Brauer.
    Rank-optimal weighting or “How to be best in the OECD Better Life Index?”.
    Social Indicators Research, 134:75-92, 2017.

    DOI: 10.1007/s11205-016-1416-0
    online at: http://arxiv.org/abs/1608.04556

34. D. Lorenz, E. Resmerita.
    Flexible sparse regularization.
    Inverse Problems, 33(1), 2016.

    DOI: 10.1088/0266-5611/33/1/014002
    online at: http://arxiv.org/abs/1601.04429

35. D. Lorenz, T. Pock.
    An inertial forward-backward method for monotone inclusions.
    Journal of Mathematical Imaging and Vision, 51(1):311-325, 2015.

    DOI: 10.1007/s10851-014-0523-2
    online at: http://arxiv.org/abs/1403.3522

36. D. Lorenz, C. Kruschel.
    Computing and analyzing recoverable supports for sparse reconstruction.
    Advances in Computational Mathematics, 41(6):1119-1144, 2015.

    DOI: 10.1007/s10444-015-9403-6
    online at: http://arxiv.org/abs/1309.2460

37. D. Lorenz, K. Bredies, S. Reiterer.
    Minimization of non-smooth, non- convex functionals by iterative thresholding.
    Journal of Optimization Theory and Applications, 165(1):78-112, 2015.

    DOI: 10.1007/s10957-014-0614-7

38. D. Lorenz, A. Tillmann, M. E. Pfetsch.
    Solving basis pursuit: Subgra- dient algorithm, heuristic optimality check, and solver comparison.
    ACM Transactions on Mathematical Software (TOMS), 41(2), 2015.

    DOI: 10.1145/2689662
    online at: http://www.optimization-online.org/DB_HTML/2011/07/3100.html

39. D. Lorenz, A. Tillmann, M. E. Pfetsch.
    An infeasible-point subgradient method using adaptive approximate projections.
    Computational Optimization and Applications, 57(2):271-306, 2014.

    DOI: 10.1007/s10589-013-9602-3
    online at: http://arxiv.org/abs/1104.5351

40. D. Lorenz, B. Komander, M. Fischer, M. Petz, R. Tutsch.
    Data fusion of surface normals and point coordinates for deflectometric measurements.
    Journal of Sensors and Sensor Systems, 3:281-290, 2014.

    DOI: 10.5194/jsss-3-281-2014

41. D. Lorenz, J. Lellmann, C. Schönlieb, T. Valkonen.
    Imaging with Kantorovich-Rubinstein discrepancy.
    SIAM Journal on Imaging Sciences, 7(4):2833-2859, 2014.

    DOI: 10.1137/140975528
    online at: http://arxiv.org/abs/1407.0221

42. D. Lorenz, M. Matz, K. Schumacher, K. Hatlapatka, K. Baumann.
    Observer-independent quantification of insulin granule exocytosis and pre-exocytotic mobility by TIRF microscopy.
    Microscopy and Microanalysis, 20(1):206-218, 2014.

    DOI: 10.1017/S1431927613013767

43. E. Herrholz, D. Lorenz, G. Teschke, D. Trede.
    Sparsity and Compressed Sensing in Inverse Problems.
    Lecture Notes in Computational Science and Engineering, 102:365-379, Springer Verlag, 2014.

    DOI: 10.1007/978-3-319-08159-5_18

44. D. Lorenz, C. Kruschel, J. S. Jørgensen.
    Testable uniqueness conditions for empirical assessment of undersampling levels in total variation-regularized x-ray CT.
    Inverse Problems in Science and Engineering, 23:1283-1305, 2014.

    DOI: 10.1080/17415977.2014.986724
    online at: http://arxiv.org/abs/1409.0214

45. D. Lorenz, S. Wenger, F. Schöpfer.
    The linearized Bregman method via split feasibility problems: Analysis and generalizations.
    SIAM Journal on Imaging Sciences, 2(7), 2014.

    DOI: 10.1137/130936269
    online at: http://arxiv.org/abs/1309.2094

46. D. Lorenz, S. Wenger, M. Magnor.
    Fast image-based modeling of astronomical nebulae.
    Computer Graphics Forum, 32(7), 2013.
47. D. Lorenz, P. Maaß, Q. M. Pham.
    Gradient descent for Tikhonov functionals with sparsity constraints: theory and numerical comparison of step size rules.
    Electronic Transactions on Numerical Analysis, 39:437-463, 2012.
48. D. Lorenz, S. Wenger, M. Ament, S. Guthe, A. Tillmann, D. Weiskopf, M. Magnor.
    Visualization of astronomical nebulae via distributed multi-gpu compressed sensing tomography.
    IEEE Transactions on Visualization and Computer Graphics, 18(12):2188-2197, 2012.

    DOI: 10.1109/TVCG.2012.281

49. D. Lorenz, S. Schiffler, D. Trede.
    Beyond convergence rates: exact recovery with the Tikhonov regularization with sparsity constraints.
    Inverse Problems, 27(8), 085009(17pp), IOPscience, 2011.

    Paper selected in "2011 Highlights for Inverse Problems"

    DOI: 10.1088/0266-5611/27/8/085009
    online at: arXiv.org e-Print archive

50. D. Lorenz, K. Chen.
    Image sequence interpolation using optimal control.
    Journal of Mathematical Imaging and Vision, 41(3):222-238, 2011.

    DOI: 10.1007/s10851-011-0274-2
    online at: http://arxiv.org/abs/1008.0548

51. D. Lorenz, E. Resmerita, K. Frick.
    Morozov’s principle for the augmented Lagrangian method applied to linear inverse problems.
    Multiscale Modeling & Simulation, 9(4):1528-1548, 2011.

    DOI: 10.1137/100812835
    online at: http://arxiv.org/abs/1010.5181

52. D. Lorenz.
    A projection proximal-point algorithm for ℓ¹ -minimization.
    Numerical Functional Analysis and Optimization, 31(2):172-190, 2010.

    DOI: 10.1080/01630560903381712
    online at: http://arxiv.org/abs/0904.1523

53. D. Lorenz, A. Rösch.
    Error estimates for joint Tikhonov- and Lavrentiev-regularization of constrained control problems.
    Applicable Analysis - An International Journal, 89(11):1679-1692, 2010.

    DOI: 10.1080/00036811.2010.496360
    online at: http://arxiv.org/abs/0909.4648

54. D. Lorenz, B. Jin.
    Heuristic parameter-choice rules for convex variational regularization based on error estimates.
    SIAM Journal on Numerical Analysis, 48(3):1208-1229, 2010.

    DOI: 10.1137/100784369
    online at: http://arxiv.org/abs/1001.5346

55. D. Lorenz, K. Chen.
    Image sequence interpolation based on optical flow, segmentation, and optimal control.
    IEEE Transactions on Image Processing, 21(3):1020-1030, 2010.

    DOI: 10.1109/TIP.2011.2179305

56. D. Lorenz, J. Lorenz.
    On conditions for convergence to consensus.
    IEEE Transactions on Automatic Control, 55(7):1651-1656, 2010.

    DOI: 10.1109/TAC.2010.2046086
    online at: http://arxiv.org/abs/0803.2211

57. K. Bredies, D. Lorenz, P. Maaß.
    A generalized conditional gradient method and its connection to an iterative shrinkage method.
    Computational Optimization and Applications, 42(2):173-193, Springer Verlag, 2009.

    DOI: 10.1007/s10589-007-9083-3

58. B. Jin, D. Lorenz, S. Schiffler.
    Elastic-Net Regularization: Error estimates and Active Set Methods.
    Inverse Problems, 25(11), 2009.

    DOI: 10.1088/0266-5611/25/11/115022

59. L. Denis, D. Lorenz, D. Trede.
    Greedy Solution of Ill-Posed Problems: Error Bounds and Exact Inversion.
    Inverse Problems, 25(11), 115017(24pp), 2009.

    DOI: 10.1088/0266-5611/25/11/115017
    online at: arXiv.org e-Print archive

60. L. Denis, D. Lorenz, E. Thiébaut, C. Fournier, D. Trede.
    Inline hologram reconstruction with sparsity constraints.
    Optics Letters, 34(22):3475-3477, 2009.

    DOI: 10.1364/OL.34.003475
    online at: HAL (Hyper Articles en Ligne) open archive

61. D. Lorenz.
    On the role of sparsity in inverse problems.
    Journal of Inverse and Ill-posed Problems, 17(1):69-76, 2009.

    DOI: 10.1515/JIIP.2009.007

62. D. Lorenz, K. Bredies.
    Regularization with non-convex separable constraints.
    Inverse Problems, 25(8), 085011, 2009.

    DOI: 10.1088/0266-5611/25/8/085011

63. D. Lorenz, R. Griesse.
    A semismooth Newton method for Tikhonov functionals with sparsity constraints.
    Inverse Problems, 24(3), 035007, 2008.

    DOI: 10.1088/0266-5611/24/3/035007
    online at: http://dx.doi.org/10.1088/0266-5611/24/3/035007

64. D. Lorenz.
    Convergence rates and source conditions for Tikhonov regularization with sparsity constraints.
    Journal of Inverse and Ill-posed Problems, 16(5):463-478, 2008.

    DOI: 10.1515/JIIP.2008.025
    online at: http://dx.doi.org/10.1515/JIIP.2008.025

65. K. Bredies, D. Lorenz.
    Iterated hard shrinkage for minimization problems with sparsity constraints.
    SIAM Journal on Scientific Computing, 30(2):657-683, 2008.
66. K. Bredies, D. Lorenz.
    Linear Convergence of iterative soft-thresholding.
    Journal of Fourier Analysis and Applications, 14(5):813-837, Springer Verlag, 2008.

    DOI: 10.1007/s00041-008-9041-1

67. K. Bredies, D. Lorenz.
    On the convergence speed of iterative methods for linear inverse problems with sparsity constraints.
    Journal of Physics, Conference Series, 124(1):2031-2043, 2008.
68. D. Lorenz, D. Trede.
    Optimal Convergence Rates for Tikhonov Regularization in Besov Scales.
    Inverse Problems, 24(5), 055010(14pp), 2008.

    DOI: 10.1088/0266-5611/24/5/055010
    online at: arXiv.org e-Print archive

69. S. Dahlke, D. Lorenz, P. Maaß, C. Sagiv, G. Teschke.
    The Canonical Coherent States Associated With Quotients of the Affine Weyl-Heisenberg Group.
    Journal of Applied Functional Analysis, 3(2):215-232, 2008.
70. K. Bredies, T. Bonesky, D. Lorenz, P. Maaß.
    A Generalized Conditional Gradient Method for Non-Linear Operator Equations with Sparsity Constraints.
    Inverse Problems, 23:2041-2058, 2007.
71. T. Bonesky, K. Bredies, D. Lorenz, P. Maaß.
    A generalized conditional gradient method for nonlinear operator equations with sparsity constraints.
    Inverse Problems, 23(5), 2007.

    DOI: 10.1088/0266-5611/23/5/014

72. D. Lorenz.
    Non-convex variational denoising of images: Interpolation between hard and soft wavelet shrinkage.
    Current Developments in Theory and Applications of Wavelets, 1(1):31-56, 2007.
73. E. Klann, D. Lorenz, P. Maaß, H. Thiele.
    Shrinkage versus Deconvolution.
    Inverse Problems, 23:2231-2248, 2007.

    DOI: 10.1088/0266-5611/23/5/025

74. D. Lorenz.
    Solving variational methods in image processing via projections - a common view on T V -denoising and wavelet shrinkage.
    Zeitschrift für angewandte Mathematik und Mechanik, 87(1):247-256, 2007.

    DOI: 10.1002/zamm200610300

75. K. Bredies, D. Lorenz, P. Maaß.
    An optimal control problem in medical image processing.
    Systems, Control, Modeling and Optimization, 202:249-259, Springer Verlag, 2006.

    DOI: 10.1007/0-387-33882-9_23

76. K. Bredies, D. Lorenz, P. Maaß.
    Mathematical Concepts of Multiscale Smoothing.
    Applied and Computational Harmonic Analysis, 19(2):141-161, Elsevier, 2005.

    DOI: 10.1016/j.acha.2005.02.007

Proceedings (12)

1. L. Tondji, D. Lorenz, I. Necoara.
   An accelerated randomized Bregman-Kaczmarz method for strongly convex linearly constraint optimization.
   2023 European Control Conference (ECC).
   

   DOI: 10.23919/ECC57647.2023.10178390

2. C. Brauer, Z. Zhao, T. Fingscheidt.
   Learning to de- quantize speech signals by primal-dual networks: an approach for acoustic sensor networks.
   IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
   

   DOI: 10.1109/ICASSP.2019.8683341

3. D. Lorenz, H. Mahler.
   Orlicz-space regularization for optimal transport and algorithms for quadratic regularization.
   NeurIPS Workshop on Optimal Transport and Machine Learning, Vancouver, Canada.
   

   online at: https://arxiv.org/abs/1909.06082

4. C. Brauer, C. Clason, D. Lorenz, B. Wirth.
   A Sinkhorn-Newton method for entropic optimal transport.
   NIPS Workshop on Optimal Transport and Machine Learning.
   

   online at: http://arxiv.org/abs/171006635

5. B. Komander, D. Lorenz.
   Denoising of image gradients and constrained total generalized variation.
   International Conference on Scale Space and Variational Methods in Computer Vision,.
   

   DOI: 10.1007/978-3-319-58771-4_35

6. C. Brauer, D. Lorenz, T. Gerkmann.
   Sparse reconstruction of quantized speech signals.
   IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
   

   DOI: 10.1109/ICASSP.2016.7472817

7. C. Brauer, D. Lorenz.
   Cartoon-texture-noise decomposition with transport norms.
   Scale Space and Variational Methods.
   LNCS, pp. 142-153, Springer Verlag, 2015.

   DOI: 10.1007/978-3-319-18461-6_12

8. D. Lorenz, F. Schöpfer, S. Wenger, M. Magnor.
   sparse Kaczmarz solver and a linearized Bregman method for online compressed sensing.
   IEEE International Conference on Image Processing.
   

   Recognized as one of the “Top 10%” papers

   DOI: 10.1109/ICIP.2014.7025269
   online at: http://arxiv.org/abs/1403.7543

9. D. Lorenz, J. Lorenz.
   Convergence to consensus by general averaging.
   POSTA 09.
   Lecture Notes in Control and Information Sciences , B. Rafael, S. Romero (Eds.), pp. 91-100, Springer Verlag, 2009.

   DOI: 10.1007/978-3-642-02894-6_9

10. D. Lorenz, K. Bredies.
    Iterated hard-thresholding for linear inverse prob- lems with sparsity constraints.
    PAMM.
    Proceedings in Applied Mathematics and Mechanics, pp. 2060061-2060062, 2008.
11. K. Bredies, D. Lorenz, P. Maaß.
    An optimal control problem in image processing.
    PAMM.
    Proceedings in Applied Mathematics and Mechanics, 6(1):859-860, 2006.

    DOI: 10.1002/pamm200610409

12. C. Brauer, D. Lorenz, L. Tondji.
    Group equivariant networks for leakage detection in vacuum bagging.
    30th European Signal Processing Confer- ence, EUSIPCO 2022.
    

    online at: https://eurasip.org/Proceedings/Eusipco/Eusipco2022/pdfs/0001437.pdf

Preprints (6)

1. M. M. Alves, D. Lorenz, E. Naldi.
   A general framework for inexact splitting algorithms with relative errors and applications to Chambolle-Pock and Davis-Yin methods.
   Zur Veröffentlichung eingereicht.

   online at: https://arxiv.org/abs/2407.05893

2. D. Lorenz, M. Winkler, A. Leitão, J. C. Rabelo.
   On inertial levenberg-marquardt type methods for solving nonlinear ill-posed operator equations.
   Zur Veröffentlichung eingereicht.

   online at: https://arxiv.org/abs/2406.07044

3. D. Nganyu Tanyu, J. Ning, A. Hauptmann, B. Jin, P. Maaß.
   Electrical Impedance Tomography: A Fair Comparative Study on Deep Learning and Analytic-based Approaches.
   Zur Veröffentlichung eingereicht.

   online at: https://arxiv.org/abs/2310.18636

4. D. Lorenz, M. Winkler.
   Minimal error momentum Bregman-Kaczmarz.
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   online at: https://arxiv.org/abs/2307.15435

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