FastAD is a C++ template library of automatic differentiation supporting both forward and reverse mode to compute gradients and Hessians. It utilizes the latest features in C++17 and expression templates for efficient computation.
- James Yang
- Kent Hall
- Provides both forward and reverse mode.
- Supports gradient and Hessian computation.
- Elementary functions have same name as their STL counterpart.
- Supports similar syntax as STL.
- MIT licensed.
- Application Server
Licensing: open source
Entries in our publication database that actually use FastAD in the numerical experiments: 0
The following diagram shows these entries versus the year of the publication.