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 201 articles , 1 standard article )

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  1. Tranquilli, Paul; Glandon, S.Ross; Sarshar, Arash; Sandu, Adrian: Analytical Jacobian-vector products for the matrix-free time integration of partial differential equations (2017)
  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. Pellegrini, Etienne; Russell, Ryan P.; Vittaldev, Vivek: $F$ and $G$ Taylor series solutions to the Stark and Kepler problems with Sundman transformations (2014)
  5. Demidenko, Eugene: Mixed models. Theory and applications with R (2013)
  6. Hascoet, Laurent; Pascual, Valérie: The Tapenade automatic differentiation tool, principles, model, and specification (2013)
  7. Patterson, Michael A.; Weinstein, Matthew; Rao, Anil V.: An efficient overloaded method for computing derivatives of mathematical functions in MATLAB (2013)
  8. Yu, Wenbin; Blair, Maxwell: DNAD, a simple tool for automatic differentiation of Fortran codes using dual numbers (2013)
  9. Bani Younes, Ahmad; Turner, James; Majji, Manoranjan; Junkins, John: High-order uncertainty propagation enabled by computational differentiation (2012)
  10. Caillau, J.-B.; Cots, O.; Gergaud, J.: Differential continuation for regular optimal control problems (2012)
  11. 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)
  12. Lülfesmann, Michael: Full and partial Jacobian computation vie graph coloring: Algorithms and applications. (2012)
  13. Narayanan, Sri Hari Krishna; Norris, Boyana; Hovland, Paul; Gebremedhin, Assefaw: Implementation of partial separability in a source-to-source transformation AD tool (2012)
  14. Phipps, Eric; Pawlowski, Roger: Efficient expression templates for operator overloading-based automatic differentiation (2012)
  15. Pi, Ting; Zhang, Yunqing; Chen, Liping: First order sensitivity analysis of flexible multibody systems using absolute nodal coordinate formulation (2012)
  16. Radul, Alexey; Pearlmutter, Barak A.; Siskind, Jeffrey Mark: AD \itin Fortran: implementation via prepreprocessor (2012)
  17. Younis, Rami M.; Tchelepi, Hamdi A.: Lazy K-way linear combination kernels for efficient runtime sparse Jacobian matrix evaluations in C++ (2012)
  18. Bücker, H.Martin; Slusanschi, Emil: Second-order derivatives of the general-purpose finite element package SEPRAN via source transformation (2011)
  19. Reid, Peter; Gamboa, Ruben: Automatic differentiation in ACL2 (2011)
  20. Toivanen, Jukka I.; Mäkinen, Raino A.E.: Implementation of sparse forward mode automatic differentiation with application to electromagnetic shape optimization (2011)

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