Forward Mode Automatic Differentiation for Julia

Forward and reverse mode automatic differentiation primitives for Julia Base + StdLibs

Next-generation AD

Reverse Mode Automatic Differentiation for Julia

Taylor polynomial expansions in one and several independent variables.

High accuracy derivatives, estimated via numerical finite differences (formerly FDM.jl)

High accuracy derivatives, estimated via numerical finite differences (formerly FDM.jl)

AD-backend agnostic system defining custom forward and reverse mode rules. This is the light weight core to allow you to define rules for your functions in your packages, without depending on any particular AD system.

Fast non-allocating calculations of gradients, Jacobians, and Hessians with sparsity support

Fast jacobian computation through sparsity exploitation and matrix coloring

An abstract interface for automatic differentiation.

Julia package for representing dual numbers and for performing dual algebra

A simple shared suite of common derivative definitions

Taylor-mode automatic differentiation for higher-order derivatives

A Cassette-based automatic differentiation package for the Julia language

Utilities for testing custom AD primitives.

Julia implementation of HyperDualNumbers

A package which provides an API for querying differentiation results at multiple orders simultaneously

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A common suite of test functions for stressing the robustness of differentiation tools.

The Documenter Theme for the ChainRules family of packages. But you can use it too

Tools to help generate operator overloads based on ChainRules