XAD is the ultimate solution for automatic differentiation, combining ease of use with high performance. It's designed to help you differentiate complex applications with speed and precision—whether you're optimizing neural networks, solving scientific problems, or performing financial risk analysis.

XAD is trusted by professionals for its speed, flexibility, and scalability across various fields:

• Machine Learning & Deep Learning: Accelerate neural network training and model optimization.
• Optimization in Engineering & Finance: Solve complex problems with high precision.
• Numerical Analysis: Improve methods for solving differential equations efficiently.
• Scientific Computing: Simulate physical systems and processes with precision.
• Risk Management & Quantitative Finance: Assess and hedge risks in sophisticated financial models.
• Computer Graphics: Optimize rendering algorithms for high-quality graphics.
• Robotics: Enhance control and simulation for robotic systems.
• Meteorology: Improve accuracy in weather prediction models.
• Biotechnology: Model complex biological processes effectively.

• Forward & Adjoint Mode: Supports any order using operator overloading.
• Checkpointing Support: Efficient tape memory management for large-scale applications.
• External Function Interface: Seamlessly connect with external libraries.
• Thread-Safe Tape: Ensure safe, concurrent operations.
• Exception-Safe: Formal guarantees for stability and error handling.
• High Performance: Optimized for speed and efficiency.
• Proven in Production: Battle-tested in large-scale, mission-critical systems.

Calculate first-order derivatives of an arbitrary function with two inputs and one output using XAD in adjoint mode.

Adouble x0 = 1.3;              // initialise inputs
Adouble x1 = 5.2;  
tape.registerInput(x0);        // register independent variables
tape.registerInput(x1);        // with the tape
tape.newRecording();           // start recording derivatives
Adouble y = func(x0, x1);      // run main function
tape.registerOutput(y);        // register the output variable
derivative(y) = 1.0;           // seed output adjoint to 1.0
tape.computeAdjoints();        // roll back adjoints to inputs
cout << "dy/dx0=" << derivative(x0) << "\n"
     << "dy/dx1=" << derivative(x1) << "\n";

git clone https://github.com/auto-differentiation/xad.git
cd xad
mkdir build
cd build
cmake ..
make

For more detailed guides, refer to our Installation Guide and explore Tutorials.

Want to get involved? We welcome contributors from all backgrounds! Check out our Contributing Guide and join the conversation in our Discussions.

Please report any issues through our Issue Tracker.

• XAD-Py: XAD in Python.
• QuantLib-Risks: Fast risk evaluations in C++ and Python.