ADOL-C

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 203 articles , 1 standard article )

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  1. 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)
  2. Baydin, Atılım Güneş; Pearlmutter, Barak A.; Radul, Alexey Andreyevich; Siskind, Jeffrey Mark: Automatic differentiation in machine learning: a survey (2018)
  3. Bell, Bradley M.; Kristensen, Kasper: Newton step methods for AD of an objective defined using implicit functions (2018)
  4. Carraro, Thomas; Dörsam, Simon; Frei, Stefan; Schwarz, Daniel: An adaptive Newton algorithm for optimal control problems with application to optimal electrode design (2018)
  5. Christianson, Bruce: Differentiating through conjugate gradient (2018)
  6. Cots, Olivier; Gergaud, Joseph; Goubinat, Damien: Direct and indirect methods in optimal control with state constraints and the climbing trajectory of an aircraft (2018)
  7. Fiege, Sabrina; Walther, Andrea; Kulshreshtha, Kshitij; Griewank, Andreas: Algorithmic differentiation for piecewise smooth functions: a case study for robust optimization (2018)
  8. Griewank, Andreas; Streubel, Tom; Lehmann, Lutz; Radons, Manuel; Hasenfelder, Richard: Piecewise linear secant approximation via algorithmic piecewise differentiation (2018)
  9. Hascoët, Laurent; Morlighem, M.: Source-to-source adjoint algorithmic differentiation of an ice sheet model written in C (2018)
  10. 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)
  11. Jurgelucks, Benjamin; Claes, Leander; Walther, Andrea; Henning, Bernd: Optimization of triple-ring electrodes on piezoceramic transducers using algorithmic differentiation (2018)
  12. Marco, Onofre; Ródenas, Juan José; Fuenmayor, Francisco Javier; Tur, Manuel: An extension of shape sensitivity analysis to an immersed boundary method based on Cartesian grids (2018)
  13. Pryce, John D.; Nedialkov, Nedialko S.; Tan, Guangning; Li, Xiao: How AD can help solve differential-algebraic equations (2018)
  14. Sagebaum, Max; Albring, T.; Gauger, N. R.: Expression templates for primal value taping in the reverse mode of algorithmic differentiation (2018)
  15. Srajer, Filip; Kukelova, Zuzana; Fitzgibbon, Andrew: A benchmark of selected algorithmic differentiation tools on some problems in computer vision and machine learning (2018)
  16. Towara, M.; Naumann, U.: SIMPLE adjoint message passing (2018)
  17. Brandt, Christopher; Hildebrandt, Klaus: Compressed vibration modes of elastic bodies (2017)
  18. Dunning, Iain; Huchette, Joey; Lubin, Miles: JuMP: a modeling language for mathematical optimization (2017)
  19. La, H. C.; Potschka, A.; Schlöder, J. P.; Bock, H. G.: Dual control and online optimal experimental design (2017)
  20. Phipps, E.; D’Elia, M.; Edwards, H. C.; Hoemmen, M.; Hu, J.; Rajamanickam, S.: Embedded ensemble propagation for improving performance, portability, and scalability of uncertainty quantification on emerging computational architectures (2017)

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