Humboldt-Universität zu Berlin - Mathematisch-Naturwissenschaftliche Fakultät - Institut für Mathematik

Publikationen - Prof. Dr. Andrea Walther

Bücher

  1. K. Fackeldey, A. Kannan, S. Pokutta, K. Sharma, D. Walter, A. Walther und M. Weiser (Eds.). Mathematical Optimization for Machine Learning (Proceedings des MATH+ TES im Sommersemester 2023), de Gruyter, erscheint in 2025.
  2. S. Forth, P. Hovland, E. Phipps, J. Utke und A. Walther (Eds.). Recent Advances in Algorithmic Differentiation (Proceedings der AD 2012 Konferenz). Lecture Notes in Computational Science and Engineering, Vol. 87 (2012).
  3. A. Griewank und A. Walther: Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation, Second Edition. SIAM (2008).

 

Preprints

  1. R. Kuess und A. Walther: On regularized structure exploiting Quasi-Newton methods for ill-posed problems. Eingereicht, hier verfügbar.
  2. L. Baumgärtner, F. Bethke, G. Shyshkanova und A. Walther: How Stringent is the Linear Independence Kink Qualification in Abs-Smooth Optimization? Eingereicht, hier verfügbar.
  3. A. Wiertz, A. Walther und G. Zöttl: Strategic retailers in the energy sector. In Überarbeitung.
  4. O. Sander und A. Walther: Two-phase calculation of second derivatives for fixed-point iterations. In Überarbeitung.
  5. R. Tannenberg, C. Hochkirch, A. Walther, S. Turnock und S. Boyd: Development of an adjoint system-based hydrofoil optimisation framework using algorithmic differentiation. In Überarbeitung.
  6. S. Tadinada, T. Siebert,  J. Fuhrmann, S. Pokutta und A. Walther: An AD-enabled Frank-Wolfe method for non-smooth optimization. In Überarbeitung.
  7. A. Hnatiuk, N. R. Gauger, J. Kusch, L. Kusch und A. Walther: Stochastic aspects of dynamical low-rank approximation in the context of machine learning. In Überarbeitung, hier verfügbar.
  8. A. Kannan, T. Kreimeier und A. Walther: On solving nonsmooth retail portfolio maximization problem using active signature methods. In Überarbeitung, hier verfügbar.

Artikel in Journalen

  1. F. Binkowski, J. Kullig, F. Betz, L. Zschiedrich, A. Walther, J. Wiersig und S. Burger: Computing eigenfrequency sensitivities near exceptional points. Erscheint in Physical Review Research.
  2. T. Kreimeier, S. Pokutta, A. Walther und Z. Woodstock: On a Frank-Wolfe Approach for Abs-smooth Functions. Erscheint in Optimization Methods and Software.
  3. F. Bethke, A. Griewank und A. Walther: A semismooth conjugate gradients method — Theoretical Analysis. Optimization Methods and Software 39(4):911-935 (2024).
  4. M. Piening, F. Altekrüger, J. Hertrich, P. Hagemann, A. Walther und G. Steidl: Learning from small data sets: Patch-based regularizers in inverse problems for image reconstruction, GAMM-Mitteilungen 47(4):e202470002 (2024).
  5. J. Bulling, B. Jurgelucks, J. Prager und A. Walther: Experimental validation of an inverse method for defect reconstruction in a two-dimensional waveguide model. Journal of the Acoustical Society of America 155, 3794–3806 (2024).
  6. L. Claes, N. Feldmann, V. Schulze, L. Meihost, H. Kuhlmann, B. Jurgelucks, A. Walther und B. Henning: Inverse procedure for measuring piezoelectricmaterial parameters using a single, multi-electrode sample. Journal of Sensors and Sensor Systems 12(1):163-173 (2023).
  7. V. Rengaraj, S. Jost, F. Bethke, C. Plessl, H. Mirhosseini, A. Walther und T. Kühne: A two-step machine learning method for predicting the thermodynamic stability of ternary compounds. Computation, Section Computational Chemistry 11(5):95 (2023).
  8. J. Bulling, B. Jurgelucks, J. Prager und A. Walther: Defect reconstruction in a 2D semi-analytical waveguide model via derivative-based optimization. Journal of the Acoustical Society 152(2):1217-1229 (2022).
  9. O. Weiß und A. Walther: A Structure Exploiting Algorithm for Non-Smooth Semi-Linear Elliptic Optimal Control Problems. Surveys in Mathematics and its Applications 17:139-179 (2022).
  10. T. Praschan, D. Heinze, D. Breddermann, A. Zrenner, A. Walther und S. Schumacher: Pulse shaping for on-demand emission of single Raman photons from a quantum-dot biexciton. Physical Review B 105:045302 (2022).
  11. A. Walther, O. Weiß, A. Griewank und S. Schmidt: Nonsmooth Optimization by Successive Abs-Linearisation in Function Spaces. Applicable Analysis 101:.225-240 (2022).
  12. N. Feldmann, V. Schulze, L. Claes, B. Jurgelucks, L. Meihost, A. Walther und B. Henning: Modelling damping in piezoceramics: A comparative study. Technisches Messen 88:294–302 (2021).
  13. N. Feldmann, V. Schulze, L. Claes, B. Jurgelucks, A. Walther und B. Henning: Inverse piezoelectric material parameter characterization using a single disc-shaped specimen. Technisches Messen 87:50-55 (2020).
  14. A. Griewank und A. Walther: Polyhedral DC decomposition and DCA optimization of piecewise linear functions. Algorithms 13(7), 166 (2020).
  15. M. Banović, I. Vasilopoulos, A. Walther und M. Meyer: Algorithmic differentiation of an industrial airfoil design tool coupled with the adjoint CFD method. Optimization and Engineering 21:1221-1242 (2020).
  16. A. Gebremedhin und A. Walther: An Introduction to Algorithmic Differentiation. WIREs Data Mining and Knowledge Discovery 10(1):e1334 (2020).
  17. S. Fiege, A. Walther und A. Griewank: An algorithm for nonsmooth optimization by successive piecewise linearization. Mathematical Programming 177(1-2):343-370 (2019).
  18. A. Walther und A. Griewank: Characterizing and testing subdifferential regularity for piecewise smooth objective functions. SIAM Journal on Optimization 29(2):1473-1501 (2019).
  19. K. Eppler, H. Harbrecht, S. Schlenkrich und A. Walther: Exterior Electromagnetic Shaping: AD-based Computation of Shape Derivatives. Journal of Mathematics Study 52(3):227-243 (2019).
  20. A. Griewank und A. Walther: Finite convergence of an active signature method to local minima of piecewise linear functions. Optimization Methods and Software 34(5):1035-1055 (2019).
  21. N. Feldmann, B. Jurgelucks, L. Claes, V. Schulze, B. Henning und A. Walther: An inverse approach to the characterisation of material parameters of piezoelectric discs with triple-ring-electrodes. Technisches Messen, 86(2):59-65 (2019).
  22. D. Richters, M. Lass, A. Walther, C. Plessl und T. Kühne: A general algorithm to calculate the inverse principal p-th root of symmetric positive definite matrices. Communications in Computational Physics, 25(2):564-585 (2019).
  23. A. Griewank und A. Walther: Relaxing kink qualifications and proving convergence rates in piecewise smooth optimization. SIAM Journal on Optimization, 29(1):262–289 (2019).
  24. O. Mykhaskiv, J.-D. Müller, P. Mohanamuraly, M. Banovic, A. Walther, S. Auriemma und H. Legrand: NURBS-based and Parametric-based Shape Optimisation with Differentiated CAD Kernel. Computer-Aided Design and Applications, 15(6):916-926 (2018).
  25. A. Walther, L. Kusch und N. Gauger: New Results for the Handling of Additional Equality Constraints in One-Shot Optimization. Vietnam Journal of Mathematics, 46:825-836 (2018).
  26. L. Kusch, T. Albring, A. Walther und N.R. Gauger: A one-shot optimization framework with additional equality constraints applied to multi-objective aerodynamic shape optimization. Optimization Methods and Software, 33(4-6):694-707 (2018).
  27. B. Jurgelucks, L. Claes, A. Walther und B. Henning: Optimisation of triple-ring electrodes on piezoceramic transducers using algorithmic differentiation. Optimization Methods and Software, 33(4-6):868-888 (2018).
  28. M. Banovic, O. Mykhaskiv, S. Auriemma, A. Walther, H. Legrand und J.-D. Müller: Automatic differentiation of the Open CASCADE Technology CAD system and its coupling with an adjoint CFD Solver. Optimization Methods and Software, 33(4-6):813-828 (2018).
  29. S. Fiege, A. Walther, K. Kulshreshtha und A. Griewank: Algorithmic differentiation for piecewise smooth functions: A case study for robust optimization. Optimization Methods and Software, 33(4-6):1073-1088 (2018).
  30. S. Schmidt, M. Schütte und A. Walther: Efficient numerical solution of geometric inverse problems involving Maxwell's equations using shape derivatives and automatic code generation. SIAM Journal of Scientific Computing, 40(2):B405-B428 (2018).
  31. A. Griewank und A. Walther: First and second order optimality conditions for piecewise smooth objective functions. Optimization Methods and Software, 31(5):904-930 (2016).
  32. A. Walther, N. Gauger, L. Kusch und N. Richert: On an extension of one-shots methods to incorporate additional constraints. Optimization Methods and Software, 31(3):494-510 (2016).
  33. A. Griewank, A. Walther, S. Fiege und T. Bosse: On Lipschitz optimization based on gray-box piecewise linearization. Mathematical Programming Series A, 158(1):383-415 (2016).
  34. A. Walther und L. Biegler: On an inexact trust-region SQP-filter method for constrained nonlinear optimization. Computational Optimization and Applications, 63(3):613-638 (2016).
  35. S. Hegler, C. Statz, M. Mütze, H. Mooshofer, M. Goldammer, K. Fendt, S. Schwarzer, K. Feldhoff, M. Flehmig, U. Markwardt, W. Nagel, M. Schütte, A. Walther, M. Meinel, A. B. und Dirk Plettemeier: Simulative Ultraschall-Untersuchung von Pitch-Catch-Messanordnungen für große zylindrische Stahl-Prüflinge und gradientenbasierte Bildgebung. Technisches Messen, 82(9):414-475 (2015).
  36. F. Bause, A. Walther, J. Rautenberg und B. Henning: Reliable computation of roots in analytical waveguide modelling using an interval-Newton approach and algorithmic differentiation. Transaction on Ultrasonics, Ferroelectrics, and Frequency Control 60(12):2597-2606 (2013).
  37. N. Gauger, A. Walther, E. Özkaya und C. Moldenhauer: Efficient aerodynamic shape optimization by structure exploitation. Optimization and Engineering 13(4):563-578 (2012).
  38. A. Walther, R. Vetukuri und L. Biegler: A first-order convergence analysis of trust-region methods with inexact Jacobians and inequality constraints. Optimisation Methods and Software 27(2):373-389 (2012).
  39. A. Griewank, K. Kulshreshtha und A. Walther: On the numerical stability of algorithmic differentiation. Computing 94:125-149 (2012).
  40. A. Walther und L. Biegler: Numerical experiments with an inexact Jacobian trust-region algorithm. Journal of Computational Optimization and Applications 48(2):255- 271 (2011).
  41. R. Vetukuri, L. Biegler und A. Walther: An Inexact Trust-Region Algorithm for the Optimization of Periodic Adsorption Processes. Industrial & Engineering Chemistry Research 49:12004–12013 (2010).
  42. M. Diehl, A. Walther, H. G. Bock und E. Kostina: An adjoint-based SQP algorithm with quasi-Newton Jacobian updates for inequality constrained optimization. Optimization Methods and Software 25(4):531-552 (2010).
  43. P. Stumm und A. Walther: New Algorithms for Optimal Online Checkpointing. SIAM Journal on Scientific Computing 32(2):836-854 (2010).
  44. M. Wagner, B.-J. Schaefer und A. Walther: On the efficient computation of high-order derivatives for implicitly defined functions. Computer Physics Communications 181:756-764 (2010).
  45. S. Schlenkrich, A. Griewank und A. Walther: On the local convergence of adjoint Broyden methods. Mathematical Programming 121(2):221-247 (2010).
  46. A. Gebremedhin, A. Pothen, A. Tarafdar und A. Walther: Efficient Computation of Sparse Hessians Using Coloring and Automatic Differentiation. INFORMS Journal on Computing 21(2):209-223 (2009).
  47. P. Stumm und A. Walther: Multi-stage Approaches for Optimal Offline Checkpointing. SIAM Journal of Scientific Computing 31(3):1946-1967 (2009).Constrained Piecewise Linear Optimization by an Active Signature Method
  48. S. Schlenkrich und A. Walther: Global convergence of quasi--Newton methods based on Adjoint Tangent Rank-1 updates. Applied Numerical Mathematics 59(5):1120-1136 (2009).
  49. A. Walther: A first-order convergence analysis of trust-region methods with inexact Jacobians. SIAM Journal on Optimization 19(1):307-325 (2008).
  50. A. Griewank, S. Schlenkrich und A. Walther: A quasi-Newton method with optimal R-order without independence assumption. Optimization Methods and Software 23(2):215-225 (2008).
  51. A. Walther: Computing Sparse Hessians with Automatic Differentiation. Transaction on Mathematical Software 34(1), Article 3 (15 pages) (2008).
  52. A. Griewank, A. Walther und M. Korzec: Maintaining factorized KKT Systems subject to Rank-one Updates of Hessians and Jacobians. Optimization Methods and Software 22(2):279-295 (2007).
  53. A. Walther: Automatic differentiation of explicit Runge-Kutta methods for optimal control. Journal of Computational Optimization and Applications 36:83-108 (2007).
  54. A. Noack und A. Walther: Adjoint concepts for the optimal control of Burgers equation. Journal of Computational Optimization and Applications 36:109-133 (2007).
  55. M. Hinze, A. Walther und J. Sternberg: An optimal memory-reduced procedure for calculating adjoints of the instationary Navier-Stokes equations. Optimal Control Applications and Methods 27(1):19-40 (2006).
  56. R. Griesse und A. Walther: Evaluating Gradients in Optimal Control - Continuous Adjoints versus Automatic Differentiation. Journal of Optimization Theory and Applications 122(1):63-86 (2004).
  57. A. Walther: Bounding the number of processes and checkpoints needed in time-minimal parallel reversal schedules. Computing 731:35-154 (2004).
  58. A. Griewank und A. Walther: How up-to-date are low-rank updates? Rev. Invest. Oper. 25:137-147 (2004).
  59. A. Walther: Program reversals for evolutions with non-uniform step costs. Acta Informatica 40:235-263 (2004).
  60. R. Griesse und A. Walther: Parametric Sensitivities for Optimal Control Problems using Automatic Differentiation. Optimal Control Applications and Methods 24(6):297-314 (2003).
  61. A. Griewank und A. Walther: On constrained optimization by adjoint based quasi-Newton methods. Optimization Methods and Software 17:869-889 (2002).
  62. A. Griewank und A. Walther: Optimal program execution reversal. Australian Mathematical Society, ANZIAM 42:C627-C652 (2000).
  63. W. Klein und A. Walther: Application of techniques of computational differentiation to a cooling system. Optimization Methods and Software 13:65-78 (2000).
  64. A. Griewank, J. Utke und A. Walther: Evaluating higher derivative tensors by forward propagation of univariate Taylor series. Mathematics of Computation 69:1117-1130 (2000).
  65. A. Griewank und A. Walther: Revolve: An implementation of checkpointing for the reverse or adjoint mode of computational differentiation. Transaction on Mathematical Software 26:19-45 (2000).
  66. A. Walther, A. Griewank und A. Best: Multiple vector-Jacobian products are cheap. Applied Numerical Mathematics 30:367-377 (1999).

 

Artikel in Tagungsbände

  1. T. Kreimeier, A. Walther und A. Griewank: Constrained Piecewise Linear Optimization by an Active Signature Method. In: Mathematical Optimization for Machine Learning (Proceedings des MATH+ TES im Sommersemester 2023), de Gruyter, erscheint 2025.
  2. G. Shyshkanova, T. Kreimeier, L. Baumgärtner, F. Bethke und A. Walther: On the nonsmooth regularity condition LIKQ for different abs-normal representations. In: Mathematical Optimization for Machine Learning (Proceedings des MATH+ TES im Sommersemester 2023), de Gruyter, erscheint 2025.
  3. R. Tannenberg, C. Hochkirch, A. Walther, S. Turnock und S. Boyd: Development of an adjoint VPP-driven hydrofoil optimisation framework. Proceedings of the 8th High Performance Yacht Design Conference (2024).
  4. G. Shyshkanova und A. Walther: Simple layer potential expansion for optimization of contact interaction taking into account friction and adhesion. Journal of Physics: Conference Series 2675, 012033 (2023).
  5. G. Shyshkanova und A. Walther: Contact pressure over doubly connected rectangular domains and punch shape optimization. AIP Conf. Proc. 2953, 060002 (2023).
  6. G. Shyshkanova und A. Walther: Optimization of a punch shape with a doubly connected contact domain. Journal of Physics: Conference Series 2609 (2023) doi:10/1088/1742-6596/2609/1/012009.
  7. T. Kreimeier, M. Kuchlbauer, F. Liers, M. Stingl und A. Walther: Towards the Solution of Robust Gas Network Optimization Problems Using the Constrained Active Signature Method. Proceedings of INOC 2022, p. 45 - 50 (2022)  doi:10.48786/inoc.2022.09.
  8. L. Claes, N. Feldmann, B. Jurgelucks, V. Schulze, S. Schmidt, A. Walther und B. Henning: Optimised Multi-Electrode Topology for Piezoelectric Material Characterisation. Sensor and Measurement Science International (2021).
  9. A. Bockhorn, K. Narayanan und A. Walther: Checkpointing Approaches for the Computation of Adjoints Covering Resilience Issues. Proceedings of the SIAM Workshop on Combinatorial Scientific Computing 2020, SIAM, pp. 22-31 (2020).
  10. S. Auriemma, M. Banovic, A. Walther, O Mykhaskiv und J.-D. Müller: Applications of differentiated CAD kernel in gradient-based aerodynamic shape optimization. Joint Propulsion Conference 2018, American Institute of Aeronautics and Astronautics, 2018-4828 (2018).
  11. S. Auriemma, M. Banovic, O. Mykhaskiv, H. Legrand, J.-D. Müller, T. Verstraete und A. Walther: Optimisation of a U-bend using a CAD-based adjoint method with differentiated CAD kernel. Proceedings des ECCOMAS Congress 2016 (2016).
  12. Y. Wang, A.W. Dowling, C. Krieft, A. Walther und L.T. Biegler: Pressure Swing Adsorption Optimization Strategies for CO2 Capture. In: Sustainability of Products, Processes and Supply Chains: Theory and Applications. Computer-Aided Chemical Engineering, 36:197-223 (2015).
  13. P. Kloke, T. Herken, V. Schöppner, J. Rudloff, K. Kretschmer, P. Heidemeyer, M. Bastian,  A. Walther und A. Dridger: Modelling of the glass fiber length and the glass fiber length distribution in the compounding of short glass fiber-reinforced thermoplastics.AIP Conference Proceedings 1593, 39 (2014).
  14. K. Feldhoff, M. Flehmig, U. Markwardt, W.E. Nagel, M. Schütte und A. Walther: SCADOPT: An Open-source HPC Framework for Solving PDE Constrained Optimization Problems Using AD. Proceedings der 2014 IEEE International Conference on High Performance Computing and Communications (HPCC), pp. 46-53 (2014).
  15. T. Steinle, J. Vrabec und A. Walther: Numerical Simulation of the Damping Behavior of Particle-Filled Hollow Spheres. Proceedings of HPSC 2012, pp. 233-244 (2014).
  16. V. Ruge, W. Braun, B. Bachmann, A. Walther und K. Kulshreshtha: Efficient Implementation of Collocation Methods for Optimization using OpenModelica and ADOL-C. In Proceedings of the 10th Modelica Conference, 1017-1025 (2014). online verfügbar
  17. F. Bause, C. Unverzagt, A. Walther und B. Henning: Utilizing interval-Newton approach for the reliablecomputation of roots in analytic waveguide modelling. Proceedings of the 2013 International Congress on Ultrasonics 2013, 504-509 (2013).
  18. M. Reichelt, A. Hildebrandt, A. Walther, J. Förstner und T. Meier: Engineering high harmonic generation in semiconductors via pulse shaping. Proc. SPIE 8260, Ultrafast Phenomena and Nanophotonics XVI, 82601L (2012).
  19. A. Walther: On the Efficient Computation of Sparsity Patterns for Hessians, Proceedings der AD 2012: Recent Advances in Algorithmic Differentiation, Lecture Notes in Computational Science and Engineering 87:139-149 (2012).
  20. B. Letschert, K. Kulshreshtha, A. Walther, D. Nguyen, A. Gebremedhin und A. Pothen: Exploiting Sparsity in Automatic Differentiation on Multicore Architectures, Proceedings der AD 2012: Recent Advances in Algorithmic Differentiation, Lecture Notes in Computational Science and Engineering 87: 151-161 (2012).
  21. M. Reichelt, A. Walther und T. Meier: Tailoring the high-harmonic emission in two-level systems and semiconductors by pulse shaping. Journal of the Optical Society of America B 29:A36–A42 (2012).
  22. N. Burschäpers, S. Fiege, R. Schuhmann und A. Walther: Sensitivity analysis of waveguide eigenvalue problems. Advances Radio Science 9:85–89, (2011).
  23. A. Walther, M. Reichelt und T. Meier: Calculus-based Optimization of Nanostructures. Photonics and Nanostructures - Fundamentals and Applications 9(4):328-336 (2011).
  24. D. Landmann, D. Plettemeier, C. Statz, F. Hoffeins, U. Markwardt, W. Nagel, A. Walther, A. Herique und W. Kofman: Three-dimensional reconstruction of comet nucleus by optimal control of Maxwell's equations: A contribution to the experiment CONSERT onboard space mission ROSETTA. Proceedings IEEE International Radar Conference 2010, Pages 1392-1396 (2010).
  25. C. Bischof, N. Guertler, A. Kowarz und A. Walther: Parallel reverse mode automatic differentiation for OpenMP programs with ADOL-C. In Chr. Bischof et al., eds.,  Proceedings AD 2008 conference, LNCSE 64, pp. 163-173, Springer (2008).
  26. U. Naumann, J. Riehme, J. Stiller und A. Walther: Adjoints for Time-Dependent Optimal Control. In Chr. Bischof et al., eds.,  Proceedings AD 2008 conference, LNCSE 64, pp. 175-185, Springer (2008).
  27. A. Gebremedhin, A. Pothen und A. Walther: Exploiting Sparsity in Jacobian Computation via Coloring and Automatic Differentiation: A Case Study in a Simulated Moving Bed Process. In Chr. Bischof et al., eds.,  Proceedings AD 2008 conference, LNCSE 64, pp. 327-338, Springer (2008).
  28. P. Stumm, A. Walther, J. Riehme und U. Naumann: Structure-exploiting Automatic Differentiation of Finite Element Discretizations. In Chr. Bischof et al., eds.,  Proceedings AD 2008 conference, LNCSE 64, pp. 339-349, Springer (2008).
  29. S. Schlenkrich, A. Walther, N.R. Gauger und R. Heinrich: Differentiating Fixed Point Iterations with ADOL-C: Gradient Calculation for Fluid Dynamics. In H.-G. Bock et al., eds., Proceedings of HPSC 2006, pp. 499-508 (2008).
  30. A. Kowarz und A. Walther: Parallel Derivative Computation Using ADOL-C. Proceedings PASA 2008, Lecture Notes in Informatics, Vol. 124, pp. 83-92(2008).
  31. N. Gauger, A. Walther, C. Moldenhauer und M. Widhalm: Automatic differentiation of an entire design chain for aerodynamic shape optimization. Notes on Numerical Fluid Mechanics and Multidisciplinary Design, Vol. 96, pp. 454-461 (2007).
  32. A. Kowarz und A. Walther: Efficient calculation of sensitivities for optimization problems. Proceedings of 3th German Polish Conference on Optimization 2005. Discussiones Mathematicae, Differential Inclusions, Control and Optimization 27, pp. 119-134 (2007).
  33. V. Heuveline und A. Walther: Online checkpointing for parallel adjoint computation in PDEs: Application to goal oriented adaptivity and flow control. In W. Nagel et al., eds., Proceedings of Euro-Par 2006, LNCS 4128, pp. 689-699, Springer (2006).
  34. A. Kowarz und A. Walther: Optimal Checkpointing for time-stepping procedures. In V. Alexandrov et al., eds., Proceedings of ICCS 2006, LNCS 3994, pp. 541-549, Springer (2006).
  35. A. Griewank und A. Walther: On the efficient generation of Taylor expansions for DAE solutions by automatic differentiation. In J. Dongarra et al., eds., Proceedings of PARA'04, LNCS 3732, pp. 1089-1098, Springer (2006).
  36. S. Schlenkrich, A. Walther und A. Griewank: AD-based quasi-Newton-Methods for the integration of stiff ODEs. In M. Bücker et al., eds., Automatic Differentiation - Applications, Theory and Implementations, LNCSE 50, pp. 89-98, Springer (2006).
  37. A. Walther und A. Griewank: Advantages of binomial checkpointing for memory-reduced adjoint calculations. Numerical Mathematics and Advanced Applications, ENUMATH 2003, Prague. M. Feistauer, V. V. Dolejsi, and P. Knobloch, and K. Najzar, eds., pp. 834-843, Springer (2004).
  38. A. Walther und A. Griewank: ADOL-C: Computing higher-order derivatives and sparsity patterns for functions written in C/C++. Proceeding of ECCOMAS Conference, P. Neittaanmäaki et al., eds., Paper 577 (14 pages) (2004).
  39. U. Naumann, J. Utke und A. Walther: An introduction to using and developing software tools for automatic differentiation. Proceeding of ECCOMAS Conference, P. Neittaanmäki et al., eds., Paper 701 (37 pages) (2004).
  40. R. Griesse und A. Walther: Using AD-generated derivatives in optimal control of an industrial robot. Progress in Industrial Mathematics at ECMI 2002, pp. 127-132, Springer (2004).
  41. A. Walther und U. Lehmann: Adjoint calculation using time-minimal program reversals for multi-processor machines. In E.W. Sachs and R. Tichatschke, eds., System Modelling and Optimization XX, pp. 317-331, Kluwer (2003).
  42. U. Lehmann und A. Walther: The implementation and testing of time-minimal and resource-optimal parallel reversal schedules. In ICCS 2002,  Proceedings of the International Conference on Computational Science, pp. 1049-1058, Springer (2002).
  43. W. Klein, A. Griewank und A. Walther: Differentiation methods for industrial strength problems. In Corliss et. al., Automatic Differentiation: From Simulation to Optimization, pp. 3-23, Springer (2001).
  44. A. Walther und A. Griewank: New results on program reversals. In Corliss et. al.,  Automatic Differentiation: From Simulation to Optimization, pp. 237-244, Springer (2001).
  45. A. Walther und A. Griewank: Applying the checkpointing routine treeverse to discretizations of Burgers' equation. In H.-J. Bungartz, F. Durst and C. Zenger, High Performance Scientific and Engineering Computing, Vol. 8 of Lecture Notes in Computational Science and Engineering, pp. 13-24, Springer (1999).

 

Kapitel in Büchern

  1. T. Kreimeier, A. Walther und  A. Griewank: Constrained piecewise linear optimization by an active signature method. Proceedings of the TES Optimization for Machine Learning (2024).
  2. G. Shyshkanova, T. Kreimeier, L. Baumgärtner, F. Bethke und A. Walther: On the nonsmooth regularity condition
    LIKQ for different abs-normal representations. Proceedings of the TES Optimization for Machine Learning (2024).
  3. O. Weiß, A. Walther und S. Schmidt: Algorithms Based on Abs-Linearization for Non-smooth Optimization with PDE Constraints. In: Non-Smooth and Complementarity-Based Distributed Parameter Systems: Simulation and Hierarchical Optimization, Springer International Publishing, pp. 377-395 (2022).
  4. A. Griewank und A. Walther: Beyond the Oracle: Opportunities of Piecewise Differentiation. In N. Karmitsa et al., Numerical nonsmooth optimization, Springer, pp. 331-362 (2020).
  5. U. Naumann und A. Walther: Combinatorial problems in algorithmic differentiation. In U. Naumann und O. Schenk, Combinatorial Scientific Computing, Chapman-Hall CRC Computational Science, pp. 129-162 (2012).
  6. A. Walther und A. Griewank: Getting started with ADOL-C. In U. Naumann und O. Schenk, Combinatorial Scientific Computing, Chapman-Hall CRC Computational Science, pp. 181-202 (2012).
  7. P. Stumm, A. Walther und D. Holfeld: Structure exploiting adjoints for finite element discretizations. In G. Leugering et al., Constrained Optimization and Optimal Control for Partial Differential Equations, ISNM160, Birkhäuser, pp.183-196 (2012).
  8. A. Walther: Wie fliegt ein Flugzeug besser? Oder: moderne Fragestellungen der nichtlinearen Optimierung. In K. Wendland und A. Werner, Facettenreiche Mathematik, Vieweg+Teubner, pp. 391-408 (2011).

 

Qualifizierungsarbeiten

  1. A. Walther: Discrete Adjoints: Theoretical Analysis, Efficient Computation, and Applications. Habilitation, TU Dresden, 2007.
  2. A. Walther: Program Reversal Schedules for Single- and Multi-processor Machines. Promotion, TU Dresden, 2000.
  3. A. Walther: Modellierung und numerische Simulation von Infrarotsensoren. Diplomarbeit, Uni Bayreuth, 1996.