autodiff
FastAD
Our great sponsors
| autodiff | FastAD | |
|---|---|---|
| 7 | 1 | |
| 1,532 | 92 | |
| 2.5% | - | |
| 7.5 | 2.5 | |
| 21 days ago | 7 months ago | |
| C++ | C++ | |
| MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
autodiff
- The Elements of Differentiable Programming
-
Astray: A performance-portable geodesic ray tracing library.
I completely agree. Specifying the metric rather than the Christoffel symbols would make it much easier for the users. Something like https://github.com/autodiff/autodiff might just work as the metric tensor is made up of primitives.
-
Point-to-Point Distance Constraint: Gradient of Forward Kinematics
Old username :D So far I have been using Eigen for linear algebra and NLOPT for optimization algorithms. I have found "autodiff" that hopefully looks easy to use: https://github.com/autodiff/autodiff
- Autodiff: Simple C++17 library for Automatic Differentiation
-
Gradients Without Backpropagation
Forward-mode differentiation is easy to implement in C++ with templates, operator overloading, and dual numbers (https://en.wikipedia.org/wiki/Automatic_differentiation#Auto...). Some libraries such as autodiff (https://github.com/autodiff/autodiff) and CppAD (https://github.com/coin-or/CppAD) use this method.
- Ensmallen: A C++ Library for Efficient Numerical Optimization
-
I am creating a fast, header-only, C++ library for control algorithms
I was thinking of adding [autodiff](https://github.com/autodiff/autodiff) in the future, mainly because it works seamlessly with *Eigen*. One big advantage would be that I could use it for AD for NonlinearSystems as well.
FastAD
-
WG21 January 2021 Mailing
What stage is the differentiation proposal in? It seems odd they talked about reverse mode AD but made no mention of memory management for it. They also don't mention anything about FastAD or Stan Math which I think do some pretty innovative things in this space.
What are some alternatives?
CppAD - A C++ Algorithmic Differentiation Package: Home Page
papers - ISO/IEC JTC1 SC22 WG21 paper scheduling and management
MathNet - Math.NET Numerics
geodesic_raytracing
MKL.NET - A simple cross platform .NET API for Intel MKL
TemporalSetInversion - Reference implementation for "Temporal Set Inversion for Animated Implicits" (SIGGRAPH 2023)
CppRobotics - Header-only C++ library for robotics, control, and path planning algorithms. Work in progress, contributions are welcome!
giada - Your Hardcore Loop Machine.
PythonRobotics - Python sample codes for robotics algorithms.
filesystem - An implementation of C++17 std::filesystem for C++11 /C++14/C++17/C++20 on Windows, macOS, Linux and FreeBSD.
Microsoft Automatic Graph Layout - A set of tools for graph layout and viewing
Mission : Impossible (AutoDiff) - A concise C++17 implementation of automatic differentiation (operator overloading)