FastAD is a C++ implementation of automatic differentiation both forward and reverse mode.
☆122Sep 27, 2023Updated 3 years ago
Alternatives and similar repositories for FastAD
Users that are interested in FastAD are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- C++ template library for probabilistic programming☆51Sep 13, 2020Updated 6 years ago
- automatic differentiation made easier for C++☆1,964Jan 27, 2025Updated last year
- ☆37Jun 29, 2017Updated 9 years ago
- Combined array and automatic differentiation library in C++☆189Oct 5, 2025Updated last year
- A Package for Automatic Differentiation of Algorithms Written in C/C++☆184Oct 22, 2026Updated last week
- GPU virtual machines on DigitalOcean Gradient AI • AdGet to production fast with high-performance AMD and NVIDIA GPUs you can spin up in seconds. The definition of operational simplicity.
- Source Code Generation for Automatic Differentiation using Operator Overloading☆203May 27, 2026Updated 5 months ago
- A C++ Algorithmic Differentiation Package: Home Page☆594Oct 7, 2026Updated 3 weeks ago
- Tutorial for the book "Algorithmic Differentiation in Finance"☆16Aug 6, 2017Updated 9 years ago
- A nim module to handle polynomials☆13Jun 7, 2022Updated 4 years ago
- SIMD vectorization backend☆17May 30, 2020Updated 6 years ago
- Companion code for "Modern Computational Finance, volume 2: Scripting for Derivatives and XVA" (Antoine Savine & Jesper Andreasen, Wiley,…☆26Oct 11, 2020Updated 6 years ago
- Rcpp Bindings to FastAD Automatic Differentiation☆13Oct 1, 2026Updated last month
- clad -- automatic differentiation for C/C++☆427Updated this week