Evaluating derivatives: principles and techniques of algorithmic differentiation A Griewank, A Walther Society for industrial and applied mathematics, 2008 | 4049 | 2008 |

Algorithm 799: revolve: an implementation of checkpointing for the reverse or adjoint mode of computational differentiation A Griewank, A Walther ACM Transactions on Mathematical Software (TOMS) 26 (1), 19-45, 2000 | 604 | 2000 |

Getting Started with ADOL-C. A Walther, A Griewank Combinatorial scientific computing 181, 202, 2009 | 295 | 2009 |

Evaluating higher derivative tensors by forward propagation of univariate Taylor series A Griewank, J Utke, A Walther Mathematics of computation 69 (231), 1117-1130, 2000 | 118 | 2000 |

On constrained optimization by adjoint based quasi-Newton methods A Griewank, A Walther Optimization Methods and Software 17 (5), 869-889, 2002 | 87 | 2002 |

Automatic differentiation of explicit Runge-Kutta methods for optimal control A Walther Computational Optimization and Applications 36 (1), 83-108, 2007 | 84 | 2007 |

Efficient computation of sparse Hessians using coloring and automatic differentiation AH Gebremedhin, A Tarafdar, A Pothen, A Walther INFORMS Journal on Computing 21 (2), 209-223, 2009 | 78 | 2009 |

An adjoint-based SQP algorithm with quasi-Newton Jacobian updates for inequality constrained optimization M Diehl, A Walther, HG Bock, E Kostina Optimization Methods & Software 25 (4), 531-552, 2010 | 53 | 2010 |

Program reversal schedules for single and multi-processor machines A Walther Dresden, Techn. Univ., Diss., 1999, 1999 | 51 | 1999 |

Multistage approaches for optimal offline checkpointing P Stumm, A Walther SIAM Journal on Scientific Computing 31 (3), 1946-1967, 2009 | 50 | 2009 |

Advantages of binomial checkpointing for memory-reduced adjoint calculations A Walther, A Griewank Numerical mathematics and advanced applications, 834-843, 2004 | 48 | 2004 |

Computing sparse Hessians with automatic differentiation A Walther ACM Transactions on Mathematical Software (TOMS) 34 (1), 1-15, 2008 | 47 | 2008 |

Evaluating gradients in optimal control: continuous adjoints versus automatic differentiation R Griesse, A Walther Journal of optimization theory and applications 122 (1), 63-86, 2004 | 47 | 2004 |

ADOL‐C: automatic differentiation using operator overloading in C++ A Walther, A Griewank, O Vogel PAMM: Proceedings in Applied Mathematics and Mechanics 2 (1), 41-44, 2003 | 46 | 2003 |

Automatic differentiation of an entire design chain for aerodynamic shape optimization NR Gauger, A Walther, C Moldenhauer, M Widhalm New Results in Numerical and Experimental Fluid Mechanics VI, 454-461, 2007 | 43 | 2007 |

First-and second-order optimality conditions for piecewise smooth objective functions A Griewank, A Walther Optimization Methods and Software 31 (5), 904-930, 2016 | 42 | 2016 |

On the efficient computation of high-order derivatives for implicitly defined functions M Wagner, A Walther, BJ Schaefer Computer Physics Communications 181 (4), 756-764, 2010 | 42 | 2010 |

New algorithms for optimal online checkpointing P Stumm, A Walther SIAM Journal on Scientific Computing 32 (2), 836-854, 2010 | 39 | 2010 |

An optimal memory‐reduced procedure for calculating adjoints of the instationary Navier‐Stokes equations M Hinze, A Walther, J Sternberg Optimal Control Applications and Methods 27 (1), 19-40, 2006 | 37 | 2006 |

Algorithmic differentiation of the Open CASCADE Technology CAD kernel and its coupling with an adjoint CFD solver M Banović, O Mykhaskiv, S Auriemma, A Walther, H Legrand, JD Müller Optimization Methods and Software 33 (4-6), 813-828, 2018 | 34 | 2018 |