autodiff/autodiff

Health v2 · maintenance only

Adoption not part of the score

1952 stars · 196 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

autodiff is a C++17 library for automatic differentiation that computes derivatives of functions efficiently using forward mode (dual numbers) and reverse mode (expression trees). It lets developers obtain gradients with minimal code changes, typically by swapping double for autodiff types.

Use cases

• compute derivatives of C++ functions automatically
• calculate gradients for optimization in C++
• evaluate partial derivatives with forward mode autodiff
• use reverse mode automatic differentiation in C++
• replace finite-difference derivative approximations with exact autodiff
• compute Jacobians and gradients for scientific computing

When to choose

• you need exact derivatives in C++ without hand-coding derivative formulas
• you want a lightweight, header-friendly C++17 alternative to frameworks like Enzyme or ADOL-C
• you need both forward and reverse mode differentiation in one library

When to avoid

• you need GPU-accelerated autodiff for large neural network training
• your project is not C++ or requires Python-first tooling like PyTorch or JAX
• you need higher-order tensor operations beyond derivative computation

Facets

library · maturity active

math machine-learning machine-learning developer-tools cpp cross-platform windows automatic-differentiation forward-mode reverse-mode dual-numbers c++17 header-only numerical-derivatives gradient-computation algorithms linux macos

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