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 221 articles , 1 standard article )

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  1. Asaithambi, Asai: Solution of third grade thin film flow using algorithmic differentiation (2020)
  2. 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)
  3. Andersson, Joel A. E.; Gillis, Joris; Horn, Greg; Rawlings, James B.; Diehl, Moritz: CasADi: a software framework for nonlinear optimization and optimal control (2019)
  4. 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)
  5. Grohs, Philipp; Hardering, Hanne; Sander, Oliver; Sprecher, Markus: Projection-based finite elements for nonlinear function spaces (2019)
  6. 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)
  7. Michael Tesch: cppduals: a nestable vectorized templated dual number library for C++11 (2019) not zbMATH
  8. Naumann, Uwe: Adjoint code design patterns (2019)
  9. Petty, David J.; Pantano, C.: A semi-Lagrangian direct-interaction closure of the spectra of isotropic variable-density turbulence (2019)
  10. Sagebaum, Max; Albring, Tim; Gauger, Nicolas R.: High-performance derivative computations using CoDiPack (2019)
  11. Sun, Yutec; Ishihara, Masakazu: A computationally efficient fixed point approach to dynamic structural demand estimation (2019)
  12. Alzetta, Giovanni; Arndt, Daniel; Bangerth, Wolfgang; Boddu, Vishal; Brands, Benjamin; Davydov, Denis; Gassmöller, Rene; Heister, Timo; Heltai, Luca; Kormann, Katharina; Kronbichler, Martin; Maier, Matthias; Pelteret, Jean-Paul; Turcksin, Bruno; Wells, David: The deal.II library, version 9.0 (2018)
  13. Banović, Mladen; Mykhaskiv, Orest; Auriemma, Salvatore; Walther, Andrea; Legrand, Herve; Müller, Jens-Dominik: Algorithmic differentiation of the Open CASCADE technology CAD kernel and its coupling with an adjoint CFD solver (2018)
  14. Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)
  15. Bell, Bradley M.; Kristensen, Kasper: Newton step methods for AD of an objective defined using implicit functions (2018)
  16. Carraro, Thomas; Dörsam, Simon; Frei, Stefan; Schwarz, Daniel: An adaptive Newton algorithm for optimal control problems with application to optimal electrode design (2018)
  17. Christianson, Bruce: Differentiating through conjugate gradient (2018)
  18. Cots, Olivier; Gergaud, Joseph; Goubinat, Damien: Direct and indirect methods in optimal control with state constraints and the climbing trajectory of an aircraft (2018)
  19. Fiege, Sabrina; Walther, Andrea; Kulshreshtha, Kshitij; Griewank, Andreas: Algorithmic differentiation for piecewise smooth functions: a case study for robust optimization (2018)
  20. Griewank, Andreas; Streubel, Tom; Lehmann, Lutz; Radons, Manuel; Hasenfelder, Richard: Piecewise linear secant approximation via algorithmic piecewise differentiation (2018)

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