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 164 articles , 1 standard article )

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  1. Ringkamp, Maik; Ober-Blöbaum, Sina; Leyendecker, Sigrid: On the time transformation of mixed integer optimal control problems using a consistent fixed integer control function (2017)
  2. Coleman, Thomas F.; Xu, Wei: Automatic differentiation in MATLAB using ADMAT with applications (2016)
  3. Gower, R.M.; Gower, A.L.: Higher-order reverse automatic differentiation with emphasis on the third-order (2016)
  4. Griewank, Andreas; Walther, Andrea; Fiege, Sabrina; Bosse, Torsten: On Lipschitz optimization based on gray-box piecewise linearization (2016)
  5. Haro, Àlex; Canadell, Marta; Figueras, Jordi-Lluís; Luque, Alejandro; Mondelo, Josep-Maria: The parameterization method for invariant manifolds. From rigorous results to effective computations (2016)
  6. Papoutsis-Kiachagias, E.M.; Giannakoglou, K.C.: Continuous adjoint methods for turbulent flows, applied to shape and topology optimization: industrial applications (2016)
  7. Sander, Oliver; Neff, Patrizio; B^ırsan, Mircea: Numerical treatment of a geometrically nonlinear planar Cosserat shell model (2016)
  8. Walther, Andrea; Biegler, Lorenz: On an inexact trust-region SQP-filter method for constrained nonlinear optimization (2016)
  9. Bonnard, Bernard; Claeys, Mathieu; Cots, Olivier; Martinon, Pierre: Geometric and numerical methods in the contrast imaging problem in nuclear magnetic resonance (2015)
  10. Naumann, Uwe; Lotz, Johannes; Leppkes, Klaus; Towara, Markus: Algorithmic differentiation of numerical methods: tangent and adjoint solvers for parameterized systems of nonlinear equations (2015)
  11. Potschka, Andreas: Direct multiple shooting for parabolic PDE constrained optimization (2015)
  12. Goldsztejn, Alexandre; Cruz, Jorge; Carvalho, Elsa: Convergence analysis and adaptive strategy for the certified quadrature over a set defined by inequalities (2014)
  13. Gower, Robert Mansel; Mello, Margarida Pinheiro: Computing the sparsity pattern of Hessians using automatic differentiation (2014)
  14. Pellegrini, Etienne; Russell, Ryan P.; Vittaldev, Vivek: $F$ and $G$ Taylor series solutions to the Stark and Kepler problems with Sundman transformations (2014)
  15. Zeng, X.; Anitescu, M.: Sequential Monte Carlo sampling in hidden Markov models of nonlinear dynamical systems (2014)
  16. Gebremedhin, Assefaw H.; Nguyen, Duc; Patwary, Md.Mostofa Ali; Pothen, Alex: ColPack, software for graph coloring and related problems in scientific computing (2013)
  17. Griewank, Andreas: On stable piecewise linearization and generalized algorithmic differentiation (2013)
  18. Hascoet, Laurent; Pascual, Valérie: The Tapenade automatic differentiation tool, principles, model, and specification (2013)
  19. Krause, Mathias J.; Heuveline, Vincent: Parallel fluid flow control and optimisation with lattice Boltzmann methods and automatic differentiation (2013)
  20. Patterson, Michael A.; Weinstein, Matthew; Rao, Anil V.: An efficient overloaded method for computing derivatives of mathematical functions in MATLAB (2013)

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