XAD
A high-performance C++ automatic differentiation library for large-scale, performance-critical systems.
What is XAD?
XAD is a fast, high-performance automatic differentiation library for C++ that computes derivatives of mathematical functions efficiently. It solves the problem of accurately and quickly calculating gradients, Jacobians, and higher-order derivatives in performance-critical applications like financial modeling, scientific computing, and machine learning.
C++ developers working on large-scale numerical applications, particularly in quantitative finance, scientific simulations, and engineering domains where derivative calculations are essential.
Developers choose XAD for its exceptional performance, low runtime overhead, and seamless integration with existing C++ codebases. Its support for both forward and reverse modes, along with optional JIT backend capabilities, makes it uniquely suited for demanding computational workloads.
Overview
Fast, easy automatic differentiation in C++
Use Cases
Best For
- Calculating financial Greeks in quantitative finance models
Related Projects
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
