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 226 articles , 1 standard article )

Showing results 1 to 20 of 226.
Sorted by year (citations)

1 2 3 ... 10 11 12 next

  1. 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)
  2. Hückelheim, J. C.; Hovland, P. D.; Strout, M. M.; Müller, J.-D.: Parallelizable adjoint stencil computations using transposed forward-mode algorithmic differentiation (2018)
  3. Dunning, Iain; Huchette, Joey; Lubin, Miles: JuMP: a modeling language for mathematical optimization (2017)
  4. Hückelheim, Jan Christian; Hascoët, Laurent; Müller, Jens-Dominik: Algorithmic differentiation of code with multiple context-specific activities (2017)
  5. Tranquilli, Paul; Glandon, S. Ross; Sarshar, Arash; Sandu, Adrian: Analytical Jacobian-vector products for the matrix-free time integration of partial differential equations (2017)
  6. Zhu, Jiamin; Trélat, Emmanuel; Cerf, Max: Geometric optimal control and applications to aerospace (2017)
  7. Coleman, Thomas F.; Xu, Wei: Automatic differentiation in MATLAB using ADMAT with applications (2016)
  8. Janka, Dennis; Kirches, Christian; Sager, Sebastian; Wächter, Andreas: An SR1/BFGS SQP algorithm for nonconvex nonlinear programs with block-diagonal Hessian matrix (2016)
  9. Papoutsis-Kiachagias, E. M.; Giannakoglou, K. C.: Continuous adjoint methods for turbulent flows, applied to shape and topology optimization: industrial applications (2016)
  10. Rump, Siegfried Michael: Floating-point arithmetic on the test bench. How are verified numerical solutions calculated? (2016)
  11. Sluşanschi, Emil I.; Dumitrel, Vlad: ADiJaC -- automatic differentiation of Java classfiles (2016)
  12. Zwicke, Florian; Knechtges, Philipp; Behr, Marek; Elgeti, Stefanie: Automatic implementation of material laws: Jacobian calculation in a finite element code with TAPENADE (2016)
  13. Janka, Dennis: Sequential quadratic programming with indefinite Hessian approximations for nonlinear optimum experimental design for parameter estimation in differential-algebraic equations (2015)
  14. Kircheis, Robert: Structure exploiting parameter estimation and optimum experimental design methods and applications in microbial enhanced oil recovery (2015)
  15. Rothe, Steffen; Hartmann, Stefan: Automatic differentiation for stress and consistent tangent computation (2015) ioport
  16. 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)
  17. Li, Xiang; Zhang, Dongxiao: A backward automatic differentiation framework for reservoir simulation (2014)
  18. Pellegrini, Etienne; Russell, Ryan P.; Vittaldev, Vivek: $F$ and $G$ Taylor series solutions to the Stark and Kepler problems with Sundman transformations (2014)
  19. Demidenko, Eugene: Mixed models. Theory and applications with R (2013)
  20. Hascoet, Laurent; Pascual, Valérie: The Tapenade automatic differentiation tool, principles, model, and specification (2013)

1 2 3 ... 10 11 12 next

Further publications can be found at: