ForwardDiff.jl

Summary:
The ForwardDiff package provides an implementation of forward-mode automatic differentiation (FAD) in Julia.

URL: https://github.com/JuliaDiff/ForwardDiff.jl

Developers:

• Jonas Rauch
• Kevin Squire
• Tom Short
• Theodore Papamarkou
• and other contributors.

Mode:	Forward
Method:	Operator overloading
Supported Language:	Julia

Reference:


Features:
ForwardDiff is undergoing development. It currently implements and will include:

1. FAD of gradients, Jacobians, Hessians and tensors, i.e. up to third-order derivatives of univariate and multivariate functions. This feature is available.

2. FAD of matrix expressions. This feature will be added in the future.

3. A range of different FAD implementations, each with varying racing merits such as range of applicability and efficiency. Two FAD approaches are available, one of which is type-based and one based on dual numbers. Two more FAD approaches will be provided, one using the box product for matrices and another one using power series.

4. A unified API across the various FAD methods. The API is operational. It will be kept up-to-date whenever new FAD algorithms are added to the package.

Licensing: open source

Entries in our publication database that actually use ForwardDiff.jl in the numerical experiments:  2

The following diagram shows these entries versus the year of the publication.

10+ #Entries 0	1	1
	'16	'17
	Year