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 188 articles , 1 standard article )

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  1. Carraro, Thomas; Dörsam, Simon; Frei, Stefan; Schwarz, Daniel: An adaptive Newton algorithm for optimal control problems with application to optimal electrode design (2018)
  2. Cots, Olivier; Gergaud, Joseph; Goubinat, Damien: Direct and indirect methods in optimal control with state constraints and the climbing trajectory of an aircraft (2018)
  3. Brandt, Christopher; Hildebrandt, Klaus: Compressed vibration modes of elastic bodies (2017)
  4. Dunning, Iain; Huchette, Joey; Lubin, Miles: JuMP: a modeling language for mathematical optimization (2017)
  5. La, H. C.; Potschka, A.; Schlöder, J. P.; Bock, H. G.: Dual control and online optimal experimental design (2017)
  6. 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)
  7. 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)
  8. Schutte, Aaron D.: A nilpotent algebra approach to Lagrangian mechanics and constrained motion (2017)
  9. Carter, Richard G.; Hossain, Shahadat; Sultana, Marzia: Efficient detection of Hessian matrix sparsity pattern (2016)
  10. Coleman, Thomas F.; Xu, Wei: Automatic differentiation in MATLAB using ADMAT with applications (2016)
  11. Gower, R. M.; Gower, A. L.: Higher-order reverse automatic differentiation with emphasis on the third-order (2016)
  12. Griewank, Andreas; Walther, Andrea; Fiege, Sabrina; Bosse, Torsten: On Lipschitz optimization based on gray-box piecewise linearization (2016)
  13. 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)
  14. Kasper Kristensen and Anders Nielsen and Casper Berg and Hans Skaug and Bradley Bell: TMB: Automatic Differentiation and Laplace Approximation (2016)
  15. Papoutsis-Kiachagias, E. M.; Giannakoglou, K. C.: Continuous adjoint methods for turbulent flows, applied to shape and topology optimization: industrial applications (2016)
  16. Rump, Siegfried Michael: Floating-point arithmetic on the test bench. How are verified numerical solutions calculated? (2016)
  17. Sander, Oliver; Neff, Patrizio; B^ırsan, Mircea: Numerical treatment of a geometrically nonlinear planar Cosserat shell model (2016)
  18. Sluşanschi, Emil I.; Dumitrel, Vlad: ADiJaC -- automatic differentiation of Java classfiles (2016)
  19. Walther, Andrea; Biegler, Lorenz: On an inexact trust-region SQP-filter method for constrained nonlinear optimization (2016)
  20. Wang, Mu; Gebremedhin, Assefaw; Pothen, Alex: Capitalizing on \itlive variables: new algorithms for efficient Hessian computation via automatic differentiation (2016)

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