ADIFOR is a tool for the automatic differentiation of Fortran 77 programs. Given a Fortran 77 source code and a user’s specification of dependent and independent variables, ADIFOR will generate an augmented derivative code that computes the partial derivatives of all of the specified dependent variables with respect to all of the specified independent variables in addition to the original result. (Source:

References in zbMATH (referenced in 255 articles , 1 standard article )

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  1. Akbarzadeh, Siamak; Hückelheim, Jan; Müller, Jens-Dominik: Consistent treatment of incompletely converged iterative linear solvers in reverse-mode algorithmic differentiation (2020)
  2. Casado, Jose Maria Varas; Hewson, Rob: Algorithm 1008: Multicomplex number class for Matlab, with a focus on the accurate calculation of small imaginary terms for multicomplex step sensitivity calculations (2020)
  3. Dolgakov, I.; Pavlov, D.: Landau: a language for dynamical systems with automatic differentiation (2020)
  4. Rockenfeller, R.; Herold, J. L.; Götz, T.: Parameter estimation and experimental design for Hill-type muscles: impulses from optimization-based modeling (2020)
  5. Brown, David A.; Zingg, David W.: Monolithic homotopy continuation with predictor based on higher derivatives (2019)
  6. Fraysse, François; Saurel, Richard: Automatic differentiation using operator overloading (ADOO) for implicit resolution of hyperbolic single phase and two-phase flow models (2019)
  7. Kharazmi, Ehsan; Zayernouri, Mohsen: Fractional sensitivity equation method: application to fractional model construction (2019)
  8. Maddison, James R.; Goldberg, Daniel N.; Goddard, Benjamin D.: Automated calculation of higher order partial differential equation constrained derivative information (2019)
  9. Naumann, Uwe: Adjoint code design patterns (2019)
  10. Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)
  11. DeGroot, Christopher T: WEdiff: A Python and C++ package for automatic differentiation (2018) not zbMATH
  12. Ghelichkhan, S.; Bunge, H.-P.: The adjoint equations for thermochemical compressible mantle convection: derivation and verification by twin experiments (2018)
  13. Hück, Alexander; Bischof, Christian; Sagebaum, Max; Gauger, Nicolas R.; Jurgelucks, Benjamin; Larour, Eric; Perez, Gilberto: A usability case study of algorithmic differentiation tools on the ISSM ice sheet model (2018)
  14. Hückelheim, J. C.; Hovland, P. D.; Strout, M. M.; Müller, J.-D.: Parallelizable adjoint stencil computations using transposed forward-mode algorithmic differentiation (2018)
  15. Marco, Onofre; Ródenas, Juan José; Fuenmayor, Francisco Javier; Tur, Manuel: An extension of shape sensitivity analysis to an immersed boundary method based on Cartesian grids (2018)
  16. Dunning, Iain; Huchette, Joey; Lubin, Miles: JuMP: a modeling language for mathematical optimization (2017)
  17. Geeraert, Sébastien; Lehalle, Charles-Albert; Pearlmutter, Barak A.; Pironneau, Olivier; Reghai, Adil: Mini-symposium on automatic differentiation and its applications in the financial industry (2017)
  18. Hückelheim, Jan Christian; Hascoët, Laurent; Müller, Jens-Dominik: Algorithmic differentiation of code with multiple context-specific activities (2017)
  19. Tranquilli, Paul; Glandon, S. Ross; Sarshar, Arash; Sandu, Adrian: Analytical Jacobian-vector products for the matrix-free time integration of partial differential equations (2017)
  20. Zhu, Jiamin; Trélat, Emmanuel; Cerf, Max: Geometric optimal control and applications to aerospace (2017)

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