ADOL-C: Automatic Differentiation of C/C++. We present two strategies for the implementation of Automatic Differentiation (AD) based on the operator overloading facility in C++. Subsequently, we describe the capabilities of the AD-tool ADOL-C that applies operator overloading to differentiate C- and C++-code. Finally, we discuss some applications of ADOL-C.

This software is also referenced in ORMS.

References in zbMATH (referenced in 239 articles , 1 standard article )

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  1. Arndt, Daniel; Bangerth, Wolfgang; Davydov, Denis; Heister, Timo; Heltai, Luca; Kronbichler, Martin; Maier, Matthias; Pelteret, Jean-Paul; Turcksin, Bruno; Wells, David: The \textscdeal.II finite element library: design, features, and insights (2021)
  2. Estévez Schwarz, Diana; Lamour, René: InitDAE: computation of consistent values, index determination and diagnosis of singularities of DAEs using automatic differentiation in Python (2021)
  3. Iavernaro, F.; Mazzia, F.; Mukhametzhanov, M. S.; Sergeyev, Ya. D.: Computation of higher order Lie derivatives on the infinity computer (2021)
  4. James Yang: FastAD: Expression Template-Based C++ Library for Fast and Memory-Efficient Automatic Differentiation (2021) arXiv
  5. Rasmussen, Atgeirr Flø; Sandve, Tor Harald; Bao, Kai; Lauser, Andreas; Hove, Joakim; Skaflestad, Bård; Klöfkorn, Robert; Blatt, Markus; Rustad, Alf Birger; Sævareid, Ove; Lie, Knut-Andreas; Thune, Andreas: The open porous media flow reservoir simulator (2021)
  6. Agamawi, Yunus M.; Rao, Anil V.: CGPOPS: a C++ software for solving multiple-phase optimal control problems using adaptive Gaussian quadrature collocation and sparse nonlinear programming (2020)
  7. Arndt, Daniel; Bangerth, Wolfgang; Blais, Bruno; Clevenger, Thomas C.; Fehling, Marc; Grayver, Alexander V.; Heister, Timo; Heltai, Luca; Kronbichler, Martin; Maier, Matthias; Munch, Peter; Pelteret, Jean-Paul; Rastak, Reza; Tomas, Ignacio; Turcksin, Bruno; Wang, Zhuoran; Wells, David: The deal.II library, version 9.2 (2020)
  8. Asaithambi, Asai: Solution of third grade thin film flow using algorithmic differentiation (2020)
  9. Banović, Mladen; Vasilopoulos, Ilias; Walther, Andrea; Meyer, Marcus: Algorithmic differentiation of an industrial airfoil design tool coupled with the adjoint CFD method (2020)
  10. Dolgakov, I.; Pavlov, D.: Landau: a language for dynamical systems with automatic differentiation (2020)
  11. Hardering, Hanne; Sander, Oliver: Geometric finite elements (2020)
  12. Jiang, Canghua; Guo, Zhiqiang; Li, Xin; Wang, Hai; Yu, Ming: An efficient adjoint computational method based on lifted IRK integrator and exact penalty function for optimal control problems involving continuous inequality constraints (2020)
  13. Jonasson, Kristjan; Sigurdsson, Sven; Yngvason, Hordur Freyr; Ragnarsson, Petur Orri; Melsted, Pall: Algorithm 1005: Fortran subroutines for reverse mode algorithmic differentiation of BLAS matrix operations (2020)
  14. Mohanamuraly, P.; Hascoët, L.; Müller, J.-D.: Seeding and adjoining zero-halo partitioned parallel scientific codes (2020)
  15. Andersson, Joel A. E.; Gillis, Joris; Horn, Greg; Rawlings, James B.; Diehl, Moritz: CasADi: a software framework for nonlinear optimization and optimal control (2019)
  16. Arndt, Daniel; Bangerth, Wolfgang; Clevenger, Thomas C.; Davydov, Denis; Fehling, Marc; Garcia-Sanchez, Daniel; Harper, Graham; Heister, Timo; Heltai, Luca; Kronbichler, Martin; Kynch, Ross Maguire; Maier, Matthias; Pelteret, Jean-Paul; Turcksin, Bruno; Wells, David: The deal.II library, Version 9.1 (2019)
  17. Bosse, Torsten: (Almost) matrix-free solver for piecewise linear functions in abs-normal form. (2019)
  18. Fraysse, François; Saurel, Richard: Automatic differentiation using operator overloading (ADOO) for implicit resolution of hyperbolic single phase and two-phase flow models (2019)
  19. Grohs, Philipp; Hardering, Hanne; Sander, Oliver; Sprecher, Markus: Projection-based finite elements for nonlinear function spaces (2019)
  20. Kiefer, Nicholas; Oremek, Maximilian J.; Hoeft, Andreas; Zenker, Sven: Model-based quantification of left ventricular diastolic function in critically ill patients with atrial fibrillation from routine data: a feasibility study (2019)

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