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 249 articles , 1 standard article )

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  1. Aubert, P.; Rousselet, B.: Sensitivity computation and shape optimization for a nonlinear arch model with limit-points instabilities (1998)
  2. Bartholomew-Biggs, M. C.: Using forward accumulation for automatic differentiation of implicitly-defined functions (1998)
  3. Bischof, Christian H.; Khademi, Peyvand M.; Bouaricha, Ali; Carle, Alan: Efficient computation of gradients and Jacobians by dynamic exploitation of sparsity in automatic differentiation (1997)
  4. Drud, Arne Stolbjerg: Interactions between nonlinear programming and modeling systems (1997)
  5. Gropp, William; Moré, Jorge J.: Optimization environments and the NEOS server (1997)
  6. Hovland, P.; Bischof, C.; Spiegelman, D.; Casella, M.: Efficient derivative codes through automatic differentiation and interface contraction: An application in biostatistics (1997)
  7. Jerrell, Max E.: Automatic differentiation and interval arithmetic for estimation of disequilibrium models (1997)
  8. Rhodin, Andreas: IMAS. Integrated modeling and analysis system for the solution of optimal control problems (1997)
  9. Campbell, Stephen L.; Hollenbeck, Richard: Automatic differentiation and implicit differential equations (1996)
  10. Gay, David M.: More AD of nonlinear AMPL models: Computing Hessian information and exploiting partial separability (1996)
  11. Geitner, Uwe; Utke, Jean; Griewank, Andreas: Automatic computation of sparse Jacobians by applying the method of Newsam and Ramsdell (1996)
  12. Griewank, A.: ODE solving via automatic differentiation and rational prediction (1996)
  13. Griewank, Andreas; Juedes, David; Utke, Jean: Algorithm 755: ADOL-C: A package for the automatic differentiation of algorithms written in C/C++ (1996)
  14. Guckenheimer, John; Myers, Mark: Computing Hopf bifurcations. II: Three examples from neurophysiology (1996)
  15. Hutschenreiter, Ulf: A new method for bevel gear tooth flank computation (1996)
  16. Juedes, David W.; Balakrishnan, Karthik: Generalized neural networks, computational differentiation, and evolution (1996)
  17. Shevchenko, A. N.; Rokityanskaya, V. N.: Automatic differentiation of functions of many variables (1996)
  18. Shiriaev, Dmitri; Griewank, Andreas: ADOL-F: Automatic differentiation of Fortran codes (1996)
  19. Dobmann, M.; Liepelt, M.; Schittkowski, K.: Algorithm 746: PCOMP: A Fortran code for automatic differentiation (1995)
  20. Masmoudi, M.; Guillaume, Ph.; Broudiscou, C.: Application of automatic differentiation to optimal shape design (1995)

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