dco/c++ implements Algorithmic Differentiation by overloading in C++. It comes with a growing number of features, e.g., derivatives of arbitrary order, vector forward and reverse mode, user-defined tangent or adjoint projections, activity analysis, disc tape, and a lot more. Additionally, dco/c++ serves as a back-end for dco/fortran.
Keywords for this software
References in zbMATH (referenced in 9 articles )
Showing results 1 to 9 of 9.
- Akbarzadeh, Siamak; Hückelheim, Jan; Müller, Jens-Dominik: Consistent treatment of incompletely converged iterative linear solvers in reverse-mode algorithmic differentiation (2020)
- Peñuñuri, F.; Peón, R.; González-Sánchez, D.; Escalante Soberanis, M. A.: Dual numbers and automatic differentiation to efficiently compute velocities and accelerations (2020)
- Naumann, Uwe: Adjoint code design patterns (2019)
- Sagebaum, Max; Albring, Tim; Gauger, Nicolas R.: High-performance derivative computations using CoDiPack (2019)
- 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)
- Kulshreshtha, K.; Narayanan, S. H. K.; Bessac, J.; MacIntyre, K.: Efficient computation of derivatives for solving optimization problems in R and Python using SWIG-generated interfaces to ADOL-C (2018)
- Beinker, Mark; Schlenkrich, Sebastian: Accurate vega calculation for Bermudan swaptions (2017)
- Naumann, Uwe; Lotz, Johannes; Leppkes, Klaus; Towara, Markus: Algorithmic differentiation of numerical methods: tangent and adjoint solvers for parameterized systems of nonlinear equations (2015)
- Lotz, Johannes; Naumann, Uwe; Ungermann, Jörn: Hierarchical algorithmic differentiation: a case study (2012)