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 238 articles , 1 standard article )

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  1. Zwicke, Florian; Knechtges, Philipp; Behr, Marek; Elgeti, Stefanie: Automatic implementation of material laws: Jacobian calculation in a finite element code with TAPENADE (2016)
  2. Janka, Dennis: Sequential quadratic programming with indefinite Hessian approximations for nonlinear optimum experimental design for parameter estimation in differential-algebraic equations (2015)
  3. Kircheis, Robert: Structure exploiting parameter estimation and optimum experimental design methods and applications in microbial enhanced oil recovery (2015)
  4. Liao, Wenyuan: An adjoint-based Jacobi-type iterative method for elastic full waveform inversion problem (2015)
  5. Rothe, Steffen; Hartmann, Stefan: Automatic differentiation for stress and consistent tangent computation (2015) ioport
  6. Brothers, Michael D.; Foster, John T.; Millwater, Harry R.: A comparison of different methods for calculating tangent-stiffness matrices in a massively parallel computational peridynamics code (2014)
  7. Li, Xiang; Zhang, Dongxiao: A backward automatic differentiation framework for reservoir simulation (2014)
  8. Pellegrini, Etienne; Russell, Ryan P.; Vittaldev, Vivek: (F) and (G) Taylor series solutions to the Stark and Kepler problems with Sundman transformations (2014)
  9. Demidenko, Eugene: Mixed models. Theory and applications with R (2013)
  10. Fang, F.; Pain, C. C.; Navon, I. M.; Cacuci, D. G.; Chen, X.: The independent set perturbation method for efficient computation of sensitivities with applications to data assimilation and a finite element shallow water model (2013)
  11. Hascoet, Laurent; Pascual, Valérie: The Tapenade automatic differentiation tool, principles, model, and specification (2013)
  12. Patterson, Michael A.; Weinstein, Matthew; Rao, Anil V.: An efficient overloaded method for computing derivatives of mathematical functions in MATLAB (2013)
  13. Sitaraman, H.; Raja, L. L.: A matrix free implicit scheme for solution of resistive magneto-hydrodynamics equations on unstructured grids (2013)
  14. Yu, Wenbin; Blair, Maxwell: DNAD, a simple tool for automatic differentiation of Fortran codes using dual numbers (2013)
  15. Bani Younes, Ahmad; Turner, James; Majji, Manoranjan; Junkins, John: High-order uncertainty propagation enabled by computational differentiation (2012)
  16. Caillau, J.-B.; Cots, O.; Gergaud, J.: Differential continuation for regular optimal control problems (2012)
  17. Fournier, David A.; Skaug, Hans J.; Ancheta, Johnoel; Ianelli, James; Magnusson, Arni; Maunder, Mark N.; Nielsen, Anders; Sibert, John: AD model builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models (2012)
  18. Lülfesmann, Michael: Full and partial Jacobian computation vie graph coloring: Algorithms and applications. (2012)
  19. Narayanan, Sri Hari Krishna; Norris, Boyana; Hovland, Paul; Gebremedhin, Assefaw: Implementation of partial separability in a source-to-source transformation AD tool (2012)
  20. Phipps, Eric; Pawlowski, Roger: Efficient expression templates for operator overloading-based automatic differentiation (2012)

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